| General Election | Total MPs | Labour MPs | Female Labour MPs | Labour MPs Intake | Intake Women | Intake Short list | Nominated Short list |
|---|---|---|---|---|---|---|---|
| 1997 | 659 | 418 | 101 (24%) | 177 | 64 (36%) | 35 | 38 |
| 2001 | 659 | 412 | 95 (23%) | 38 | 4 (11%) | 0 | 0 |
| 2005 | 646 | 355 | 98 (28%) | 40 | 26 (65%) | 23 | 30 |
| 2010 | 650 | 258 | 81 (31%) | 64 | 32 (50%) | 28 | 63 |
| 2015 | 650 | 232 | 99 (43%) | 49 | 31 (63%) | 31 | 77 |
Data in this table is from House of Commons library reports (Audickas, Hawkins, & Cracknell, 2017; Kelly, 2016). All women short lists were not used by Labour during the 2001 General Election.
| Gender | Speeches | Words |
|---|---|---|
| All | 656,412 | 111,180,398 |
| Female | 148,702 | 26,231,034 |
| Male | 507,710 | 84,949,364 |
| Conservatives | ||
| All | 285,291 | 44,800,169 |
| Female | 48,768 | 7,363,031 |
| Male | 236,523 | 37,437,138 |
| Labour | ||
| All | 261,942 | 46,494,850 |
| Female | 84,569 | 15,897,929 |
| Non-All Women Shortlists | 28,695 | 5,422,776 |
| All Women Shortlists | 55,874 | 10,475,153 |
| Male | 177,373 | 30,596,921 |
| Liberal Democrat | ||
| All | 72,716 | 13,485,902 |
| Female | 7,552 | 1,503,459 |
| Male | 65,164 | 11,982,443 |
| Other | ||
| All | 36,463 | 6,399,477 |
| Female | 7,813 | 1,466,615 |
| Male | 28,650 | 4,932,862 |
Previous research on gender differences in political speech patterns has focused on differences between male and female politicians (Yu, 2014) or on variations in Hilary Clinton’s speech patterns (Bligh, Merolla, Schroedel, & Gonzalez, 2010; Jones, 2016). This paper focuses on differences in speech patterns between female Labour MPs nominated through All Women Shortlists (AWS) and female Labour MPs nominated through open short lists. We examined differences in speaking styles using the Linguistic Inquiry and Word Count 2015 (LIWC) dictionary (Pennebaker, Boyd, Jordan, & Blackburn, 2015) and the spaCy (Honnibal & Montani, 2017) Parts-of-Speech (POS) tagger. We examined differences in the topics discussed by AWS and non-AWS MPs, using \({\chi}^2\) tests for individual words and for bigrams. We trained a Naive Bayes classifier to distinguish AWS and non-AWS speeches. We used structured topic models (STM) to identify the topics discussed by AWS and non-AWS MPs.
To account for the possible effects of age, parliamentary experience and cohort, and in order to compare women selected through all women short lists to women who were not (but who theoretically had the opportunity to contest all-women short lists), our analysis is been restricted only to Labour MPs first elected to the House of Commons in the 1997 General Election, up to but excluding the 2017 General Election. Comparisons between MPs of different parties are also restricted to MPs first elected in the 1997 General Election, and before the 2017 General Election. Speeches made by the Speaker, including Deputy Speakers, were also excluded. Words contained in parentheses were removed, as they are added by Hansard to provide additional information not actually spoken by the MP.1 Speeches and data on MPs’ gender and party affiliation are from a previously assembled dataset (Odell, 2018). Information on candidates selected through all women short lists is from the House of Commons Library (Kelly, 2016). Unsuccessful General Election candidates selected through all women short lists who were subsequently elected in a byelection are classified as having been selected on an all women short list.
Word classification used the Linguistic Inquiry and Word Count 2015 (LIWC) dictionary (Pennebaker et al., 2015) and tokenising tools from the Quanteda R package (Benoit, 2018). Word counts and words-per-sentence were calculated using stringi (Gagolewski, 2018), a wrapper to the ICU regex library.
Following Yu (2014) drawing on Newman, Groom, Handelman, & Pennebaker (2008) we used the following LIWC categories:
We also included mean words-per-sentence (WPS), total speach word count (WC) and Flesch–Kincaid grade level (FK) (Kincaid, Fishburne, Rogers, & Chissom, 1975), calculated using Quanteda (Benoit, 2018) and stringi (Gagolewski, 2018).
| Mean | SD | Mean | SD | Cohen’s D | Magnitude | |
|---|---|---|---|---|---|---|
| All Pronouns | 10.07 | 4.60 | 10.15 | 4.99 | 0.02 | negligible |
| First person singular pronouns | 1.89 | 2.41 | 2.02 | 2.55 | 0.05 | negligible |
| First person plural pronouns | 0.97 | 1.42 | 0.99 | 1.51 | 0.01 | negligible |
| Verbs | 12.82 | 5.00 | 12.67 | 5.36 | -0.03 | negligible |
| Auxiliary verbs | 7.91 | 3.45 | 7.93 | 3.69 | 0.01 | negligible |
| Social processes | 8.47 | 4.82 | 8.18 | 5.11 | -0.06 | negligible |
| Positive emotions | 2.73 | 2.49 | 2.57 | 2.54 | -0.06 | negligible |
| Negative emotions | 1.15 | 1.68 | 1.07 | 1.77 | -0.05 | negligible |
| Tentative words | 1.48 | 1.74 | 1.58 | 1.90 | 0.05 | negligible |
| More than six letters | 10.58 | 3.68 | 10.22 | 3.92 | -0.10 | negligible |
| Articles | 7.65 | 3.30 | 7.96 | 3.55 | 0.09 | negligible |
| Prepositions | 12.58 | 4.42 | 12.14 | 4.73 | -0.10 | negligible |
| Anger words | 0.23 | 0.81 | 0.24 | 0.77 | 0.01 | negligible |
| Swear words | 0.00 | 0.06 | 0.00 | 0.09 | 0.01 | negligible |
| Cognitive processes | 8.68 | 4.83 | 8.82 | 5.15 | 0.03 | negligible |
| Words per Sentence | 43.63 | 19.68 | 41.15 | 20.04 | -0.13 | negligible |
| Total Word Count | 402.79 | 691.27 | 370.18 | 647.36 | -0.05 | negligible |
| Flesh-Kincaid Grade Level | 10.81 | 7.68 | 9.78 | 7.87 | -0.13 | negligible |
There are no categories where gender differences meet the effect size threshold of \(|0.2|\) suggested by Cohen (1988, pp. 25–26) to indicate a small effect. 4 categories – words with more than six letters, prepositions, words-per-sentence and Flesh-Kincaid grade level – exceeded the \(|0.1|\) threshold suggested by Newman et al (2008).
The following plots show changes in the occurences of selected LIWC terms, words-per-sentence, total word count and Flesch–Kincaid grade level, over the course of an MP’s career. There do not appear to be any notable changes in speaking style over the course of female Labour MPs’ careers.
Occurence of selected LIWC terms
| Mean | SD | Mean | SD | Cohen’s D | Magnitude | |
|---|---|---|---|---|---|---|
| All Pronouns | 10.01 | 4.67 | 10.19 | 4.48 | -0.04 | negligible |
| First person singular pronouns | 1.86 | 2.41 | 1.95 | 2.42 | -0.04 | negligible |
| First person plural pronouns | 0.88 | 1.36 | 1.16 | 1.51 | -0.19 | negligible |
| Verbs | 12.88 | 5.10 | 12.69 | 4.80 | 0.04 | negligible |
| Auxiliary verbs | 7.94 | 3.49 | 7.86 | 3.38 | 0.02 | negligible |
| Social processes | 8.48 | 4.94 | 8.46 | 4.59 | 0.00 | negligible |
| Positive emotions | 2.69 | 2.52 | 2.81 | 2.42 | -0.05 | negligible |
| Negative emotions | 1.16 | 1.69 | 1.13 | 1.67 | 0.02 | negligible |
| Tentative words | 1.48 | 1.75 | 1.49 | 1.73 | 0.00 | negligible |
| More than six letters | 10.52 | 3.73 | 10.70 | 3.58 | -0.05 | negligible |
| Articles | 7.69 | 3.38 | 7.55 | 3.15 | 0.04 | negligible |
| Prepositions | 12.55 | 4.54 | 12.63 | 4.15 | -0.02 | negligible |
| Anger words | 0.23 | 0.78 | 0.24 | 0.88 | -0.01 | negligible |
| Swear words | 0.00 | 0.06 | 0.00 | 0.05 | 0.01 | negligible |
| Cognitive processes | 8.59 | 4.90 | 8.86 | 4.69 | -0.06 | negligible |
| Words per Sentence | 44.02 | 20.45 | 42.85 | 18.05 | 0.06 | negligible |
| Total Word Count | 401.77 | 704.40 | 404.78 | 664.97 | 0.00 | negligible |
| Flesh-Kincaid Grade Level | 10.97 | 7.97 | 10.48 | 7.06 | 0.07 | negligible |
There are no categories among female Labour MPs by selection process meeting the \(|0.2|\) threshold. Only one category – first person plural pronouns, d=0.19 – exceeded \(|0.1|\).
| Mean | SD | Mean | SD | Cohen’s D | Magnitude | |
|---|---|---|---|---|---|---|
| All Pronouns | 10.12 | 4.87 | 10.62 | 4.84 | 0.10 | negligible |
| First person singular pronouns | 1.98 | 2.51 | 2.15 | 2.56 | 0.07 | negligible |
| First person plural pronouns | 0.98 | 1.48 | 1.22 | 1.70 | 0.15 | negligible |
| Verbs | 12.72 | 5.24 | 12.93 | 5.13 | 0.04 | negligible |
| Auxiliary verbs | 7.92 | 3.61 | 8.17 | 3.58 | 0.07 | negligible |
| Social processes | 8.28 | 5.02 | 8.13 | 4.80 | -0.03 | negligible |
| Positive emotions | 2.62 | 2.53 | 2.85 | 2.66 | 0.09 | negligible |
| Negative emotions | 1.10 | 1.74 | 1.05 | 1.78 | -0.03 | negligible |
| Tentative words | 1.55 | 1.85 | 1.57 | 1.88 | 0.01 | negligible |
| More than six letters | 10.34 | 3.85 | 10.28 | 3.76 | -0.02 | negligible |
| Articles | 7.86 | 3.48 | 7.82 | 3.45 | -0.01 | negligible |
| Prepositions | 12.28 | 4.64 | 12.38 | 4.49 | 0.02 | negligible |
| Anger words | 0.24 | 0.78 | 0.24 | 0.82 | 0.01 | negligible |
| Swear words | 0.00 | 0.08 | 0.00 | 0.10 | 0.00 | negligible |
| Cognitive processes | 8.77 | 5.05 | 8.86 | 5.06 | 0.02 | negligible |
| Words per Sentence | 41.95 | 19.96 | 42.76 | 20.16 | 0.04 | negligible |
| Total Word Count | 380.71 | 662.03 | 335.54 | 592.41 | -0.07 | negligible |
| Flesh-Kincaid Grade Level | 10.12 | 7.82 | 10.41 | 7.91 | 0.04 | negligible |
There are no categories with effect sizes exceeding \(|0.2|\) between Labour and Conservative MPs, like inter-Labour differences.
There are no categories with effect sizes exceeding \(|0.2|\) when comparing all male and female MPs elected from 1997 onwards. There is only one category, “Articles”, with an effect size of 0.11, greater than the \(|0.1|\) threshold suggested by Newman et al. (2008).
| Mean | SD | Mean | SD | Cohen’s D | Magnitude | |
|---|---|---|---|---|---|---|
| All Pronouns | 10.31 | 4.65 | 10.26 | 4.90 | -0.01 | negligible |
| First person singular pronouns | 1.99 | 2.45 | 2.00 | 2.52 | 0.00 | negligible |
| First person plural pronouns | 1.11 | 1.57 | 1.08 | 1.59 | -0.02 | negligible |
| Verbs | 12.88 | 4.97 | 12.80 | 5.26 | -0.02 | negligible |
| Auxiliary verbs | 8.00 | 3.45 | 8.08 | 3.64 | 0.02 | negligible |
| Social processes | 8.45 | 4.77 | 8.00 | 4.93 | -0.09 | negligible |
| Positive emotions | 2.84 | 2.53 | 2.69 | 2.58 | -0.06 | negligible |
| Negative emotions | 1.10 | 1.65 | 1.08 | 1.78 | -0.01 | negligible |
| Tentative words | 1.47 | 1.73 | 1.61 | 1.91 | 0.08 | negligible |
| More than six letters | 19.73 | 6.94 | 19.25 | 7.18 | -0.07 | negligible |
| Articles | 7.62 | 3.31 | 8.00 | 3.51 | 0.11 | negligible |
| Prepositions | 12.58 | 4.36 | 12.22 | 4.62 | -0.08 | negligible |
| Anger words | 0.23 | 0.78 | 0.25 | 0.82 | 0.02 | negligible |
| Swear words | 0.00 | 0.05 | 0.00 | 0.10 | 0.01 | negligible |
| Cognitive processes | 8.67 | 4.79 | 8.93 | 5.12 | 0.05 | negligible |
| Words per Sentence | 43.25 | 19.45 | 42.06 | 20.12 | -0.06 | negligible |
| Total Word Count | 377.31 | 648.92 | 358.13 | 623.49 | -0.03 | negligible |
| Flesh-Kincaid Grade Level | 10.63 | 7.61 | 10.16 | 7.89 | -0.06 | negligible |
| Word Type | Mean | SD | Mean | SD | Cohen’s D | Magnitude |
|---|---|---|---|---|---|---|
| All Nouns | 22.18 | 9.60 | 21.66 | 10.96 | -0.05 | negligible |
| Plural Nouns | 5.85 | 3.72 | 5.03 | 3.79 | -0.22 | small |
| Singular Nouns | 15.62 | 9.84 | 16.01 | 11.19 | 0.04 | negligible |
| Adjectives | 9.58 | 4.78 | 9.28 | 5.29 | -0.06 | negligible |
| Adverbs | 4.91 | 4.26 | 5.07 | 4.91 | 0.03 | negligible |
| Verbs | 20.94 | 9.52 | 20.78 | 10.28 | -0.02 | negligible |
| Word Type | Mean | SD | Mean | SD | Cohen’s D | Magnitude |
|---|---|---|---|---|---|---|
| All Nouns | 22.16 | 8.78 | 22.18 | 10.00 | -0.04 | negligible |
| Plural Nouns | 6.03 | 3.60 | 5.76 | 3.77 | -0.16 | negligible |
| Singular Nouns | 15.51 | 8.97 | 15.67 | 10.26 | 0.02 | negligible |
| Adjectives | 9.83 | 4.59 | 9.45 | 4.86 | -0.02 | negligible |
| Adverbs | 4.95 | 3.78 | 4.89 | 4.49 | 0.03 | negligible |
| Verbs | 20.88 | 9.04 | 20.97 | 9.76 | -0.02 | negligible |
Part-of-speech (POS) tagging was done using spaCy (Honnibal & Montani, 2017) and the spacyr package (Benoit & Matsuo, 2018). There is one small gender difference (d = \(|0.22|\)) in the use of plural nouns, which make up 5.85% of the words used by female Labour MPs, compared to 5.03% of words spoken by male Labour MPs. As with LIWC, there are no categories where d >= \(|0.2|\) when comparing female Labour MPs by selection process.
The most commonly used words by both men and women would be protocol decorum expressions, so we calculate the keyness of words to identify gender differences in the choices of topics raised by men and women, and by short-list and non-short list women.
Keyness – a linguistic measure of the frequency of different words in two groups of texts – reveals clear gender differences in the most disproportionately common words used by female and male Labour MPs. Unsurprisingly, despite male MPs saying almost twice as many words (30,596,921 vs 15,897,929) as their female colleagues, female Labour MPs were more than two-and-a-half (2.61) times as likely to say “women”. They were also much more likely to use “women’s” and “woman” in parliamentary debate. Female Labour MPs also appear much more likely to discuss “children”, “people”, “care”, “families”, “home”, “parents”, “work” and social policy areas such as “services”, “disabled [people]” and “housing” than their male colleagues. Male MPs were more likely to refer to military topics (“Iraq”, “nuclear”), and to parliamentary process and protocol – “question”, “political”, “conservative”, “electoral”, “house”, “party”, “argument” “liberal” and “point” are far more common in speeches by male Labour MPs than by female ones. This could suggest that male Labour MPs are more comfortable using the traditional language of House of Commons debate, and are more concerned with the rules, procedures and processes of the parliamentary system than their female colleagues.
Keyness between Labour MPs, by Gender
Keyness differences by selection process are not as obviously stereotypical. Nonetheless, the most common words amongst AWS MPs included “carers”, “disabled”, “bedroom” and “sen”2. Also of note is AWS MPs making more references to their “constituency” and its “constituents”, suggesting that AWS MPs may draw on the fact they were elected by their constituents as a source political legitimacy, at least more than non-AWS MPs.
Keyness between Female Labour MPs, by Selection Process
The keyness differences between Labour and Conservative MPs are much greater than gender differences within Labour. The very high use of “Lady” by Conservative MPs is reflective of the greater proportion of female MPs in other parties, as it is often used to refer to comments by other members of the house. It may also represent a greater use of traditional hosue decorum by Conservative MPs.
Keyness between Labour and Conservative MPs
We created bigrams of all first person plural and singular pronouns for female Labour MPs. As above, AWS MPs are far more likely to make references to their constituency or their constituents.
Bigram Keyness in Female Labour MPs by Selection Process
We trained a Naive Bayes classifier with document-frequency priors and a multinomial distribution to predict the gender of speakers when given speeches by all Labour MPs in our dataset, and the selection process when only given female Labour MPs. The accuracy of both models were roughly equivalent, 70.55% accuracy when predicting gender and 70.84% when predicting short lists. By contrast, the classifier could distinguish between Labour and Conservative speeches with 74.23% accuracy.
Using topic models to classify text is widely used in social sciences (Grimmer & Stewart, 2013), as, when combined with the large volume of plain text data available, it allows for a rapid and consistent method of analysis . Topic modelling and other statistic methods of textual analysis are not a substitute for reading the texts themselves, but can augment other analysis or – as in this case – analyse and classify larger amounts of text than would be feasible using human coders (Grimmer & Stewart, 2013). Topic models classify a series of documents (in this case individual speeches) into one of a given number of topics, identifying terms that are common in some documents but rare in others. When developing topic models, there is a trade-off between high precision in the classification of each document with broader topics when using smaller numbers of topics, or lower precision in individual speech classification with more finely-grained topics when using larger numbers of topics. Grimmer & Stewart (2013) also highlight the importance of validating unsurpervised topic models when applied to new sets of texts.
The R package stm (Roberts, Stewart, & Tingley, 2018) implements a structured topic model (STM) (Arora et al., 2013; Roberts, Stewart, & Airoldi, 2016). An STM incorporates data about the writer or speaker into the topic classification algorithm. This differs from traditional topic modelling methods using latent variables to identify topics (e.g. with latent Dirichlet allocation Blei, Ng, & Jordan, 2003), and then comparing proportions of each topic to one or more external variables. STM allows us to incorporate the variables we are interested in to the topic model itself, i.e. the proportion of speechs classified as belonging to each topic can vary as a function of the AWS variable.
We incorporated the AWS status of speakers into our topic model, using all speeches by female Labour MPs, with their AWS status as a covariate in classifying topics. We then matched these topics to speechs by male Labour MPs.
We produced two different structured topic model implementations, with different numbers of topics (K). For each implementation, we created Fruchterman-Reingold (Fruchterman & Reingold, 1991) diagrams. Larger vertices indicate more common topics, and the plot uses a colour scale to indicate the proportion of speeches classed in that topic made by AWS and non-AWS female Labour MPs. The space between vertices indicate the closeness of two topics.
The first implementation used an algorithm developed by Lee & Mimno (2014), implemented in the stm package (Roberts et al., 2018), to estimate the number of topics across all speeches made by female Labour MPs, using the “spectral” method developed by Arora et al. (2013), implemented by Roberts et al. (2018). The resulting topic model has 69 topics, across 81,607 documents and a dictionary of 115,477 words. However, the topic quality with K = 69 is poor, and several topics have poor semantic coherence (see ).
There are several clusters of topics in . For instance, we can see the closeness of Topic 15 (economics and government budgets) and Topic 43 (housing), as both include discussions of budgets and costs, while Topics 23 (bill clauses and admendments) and 16 (education) are very far apart.
Fruchterman-Reingold plot of K69 Network
Coherence of K69 Topic Models
| Topic Number | AWS Speeches | Percent of AWS Speeches | Non-AWS Speeches | Percent of non-AWS Speeches | Male MP Speeches | Percent of Male MP Speeches |
|---|---|---|---|---|---|---|
| Topic 1 | 1,272 | 2.37% | 353 | 1.27% | 3,434 | 2.03% |
| Topic 2 | 334 | 0.62% | 127 | 0.46% | 1,091 | 0.64% |
| Topic 3 | 241 | 0.45% | 71 | 0.25% | 427 | 0.25% |
| Topic 4 | 550 | 1.02% | 133 | 0.48% | 835 | 0.49% |
| Topic 5 | 826 | 1.54% | 206 | 0.74% | 2,452 | 1.45% |
| Topic 6 | 978 | 1.82% | 915 | 3.28% | 4,060 | 2.4% |
| Topic 7 | 648 | 1.21% | 236 | 0.85% | 1,770 | 1.05% |
| Topic 8 | 70 | 0.13% | 25 | 0.09% | 125 | 0.07% |
| Topic 9 | 265 | 0.49% | 309 | 1.11% | 862 | 0.51% |
| Topic 10 | 1,024 | 1.91% | 513 | 1.84% | 1,065 | 0.63% |
| Topic 11 | 940 | 1.75% | 580 | 2.08% | 3,793 | 2.24% |
| Topic 12 | 313 | 0.58% | 319 | 1.14% | 1,309 | 0.77% |
| Topic 13 | 325 | 0.61% | 146 | 0.52% | 1,181 | 0.7% |
| Topic 14 | 1,596 | 2.97% | 461 | 1.65% | 2,885 | 1.7% |
| Topic 15 | 1,386 | 2.58% | 642 | 2.3% | 4,686 | 2.77% |
| Topic 16 | 1,407 | 2.62% | 525 | 1.88% | 3,651 | 2.16% |
| Topic 17 | 3,690 | 6.87% | 1,459 | 5.23% | 19,358 | 11.43% |
| Topic 18 | 1,026 | 1.91% | 847 | 3.04% | 4,760 | 2.81% |
| Topic 19 | 640 | 1.19% | 423 | 1.52% | 2,130 | 1.26% |
| Topic 20 | 872 | 1.62% | 216 | 0.77% | 2,262 | 1.34% |
| Topic 21 | 658 | 1.23% | 363 | 1.3% | 914 | 0.54% |
| Topic 22 | 818 | 1.52% | 439 | 1.57% | 1,965 | 1.16% |
| Topic 23 | 795 | 1.48% | 518 | 1.86% | 3,553 | 2.1% |
| Topic 24 | 385 | 0.72% | 199 | 0.71% | 1,079 | 0.64% |
| Topic 25 | 240 | 0.45% | 74 | 0.27% | 422 | 0.25% |
| Topic 26 | 788 | 1.47% | 200 | 0.72% | 1,738 | 1.03% |
| Topic 27 | 266 | 0.5% | 120 | 0.43% | 1,010 | 0.6% |
| Topic 28 | 847 | 1.58% | 350 | 1.25% | 3,135 | 1.85% |
| Topic 29 | 1,110 | 2.07% | 327 | 1.17% | 944 | 0.56% |
| Topic 30 | 1,132 | 2.11% | 462 | 1.66% | 6,444 | 3.81% |
| Topic 31 | 996 | 1.85% | 975 | 3.49% | 6,078 | 3.59% |
| Topic 32 | 76 | 0.14% | 64 | 0.23% | 335 | 0.2% |
| Topic 33 | 1,238 | 2.31% | 985 | 3.53% | 6,613 | 3.9% |
| Topic 34 | 1,124 | 2.09% | 521 | 1.87% | 3,335 | 1.97% |
| Topic 35 | 650 | 1.21% | 657 | 2.35% | 2,294 | 1.35% |
| Topic 36 | 601 | 1.12% | 154 | 0.55% | 548 | 0.32% |
| Topic 37 | 455 | 0.85% | 194 | 0.7% | 1,554 | 0.92% |
| Topic 38 | 1,246 | 2.32% | 991 | 3.55% | 2,849 | 1.68% |
| Topic 39 | 1,917 | 3.57% | 936 | 3.35% | 7,664 | 4.53% |
| Topic 40 | 848 | 1.58% | 290 | 1.04% | 2,419 | 1.43% |
| Topic 41 | 63 | 0.12% | 40 | 0.14% | 204 | 0.12% |
| Topic 42 | 853 | 1.59% | 590 | 2.11% | 2,016 | 1.19% |
| Topic 43 | 1,344 | 2.5% | 604 | 2.16% | 2,266 | 1.34% |
| Topic 44 | 814 | 1.52% | 288 | 1.03% | 3,005 | 1.77% |
| Topic 45 | 602 | 1.12% | 474 | 1.7% | 1,086 | 0.64% |
| Topic 46 | 709 | 1.32% | 150 | 0.54% | 1,646 | 0.97% |
| Topic 47 | 664 | 1.24% | 245 | 0.88% | 2,992 | 1.77% |
| Topic 48 | 940 | 1.75% | 901 | 3.23% | 3,045 | 1.8% |
| Topic 49 | 835 | 1.55% | 563 | 2.02% | 2,537 | 1.5% |
| Topic 50 | 1,328 | 2.47% | 1,219 | 4.37% | 3,421 | 2.02% |
| Topic 51 | 1,076 | 2% | 323 | 1.16% | 2,453 | 1.45% |
| Topic 52 | 196 | 0.36% | 85 | 0.3% | 758 | 0.45% |
| Topic 53 | 590 | 1.1% | 293 | 1.05% | 746 | 0.44% |
| Topic 54 | 1,057 | 1.97% | 824 | 2.95% | 5,570 | 3.29% |
| Topic 55 | 302 | 0.56% | 157 | 0.56% | 868 | 0.51% |
| Topic 56 | 535 | 1% | 398 | 1.43% | 847 | 0.5% |
| Topic 57 | 656 | 1.22% | 314 | 1.13% | 1,990 | 1.18% |
| Topic 58 | 468 | 0.87% | 182 | 0.65% | 1,125 | 0.66% |
| Topic 59 | 426 | 0.79% | 183 | 0.66% | 700 | 0.41% |
| Topic 60 | 562 | 1.05% | 297 | 1.06% | 1,389 | 0.82% |
| Topic 61 | 86 | 0.16% | 28 | 0.1% | 174 | 0.1% |
| Topic 62 | 550 | 1.02% | 343 | 1.23% | 746 | 0.44% |
| Topic 63 | 690 | 1.28% | 252 | 0.9% | 1,726 | 1.02% |
| Topic 64 | 594 | 1.11% | 244 | 0.87% | 2,247 | 1.33% |
| Topic 65 | 662 | 1.23% | 457 | 1.64% | 907 | 0.54% |
| Topic 66 | 1,493 | 2.78% | 527 | 1.89% | 4,073 | 2.41% |
| Topic 67 | 737 | 1.37% | 451 | 1.62% | 3,237 | 1.91% |
| Topic 68 | 279 | 0.52% | 145 | 0.52% | 547 | 0.32% |
| Topic 69 | 1 | 0% | NA | NA% | NA | NA% |
K69 Pyramid Chart
K69 Bar Chart
The table below shows the ten most common words in each topic, and the ten words with the highest FREX score, a measure that uses a harmonic mean of word exclusivity and topic coherence (Airoldi & Bischof, 2016).
| Topic Number | Top Ten Words | Top Ten FREX |
|---|---|---|
| Topic 1 | secretary, state, tell, ministers, given, today, department, can, confirm, said | secretary, state, confirm, tell, ministers, state’s, minister’s, explain, please, discussions |
| Topic 2 | safety, register, registration, indicated, registered, electoral, risk, risks, number, individual | registration, indicated, hse, canvass, register, gurkhas, safety, dissent, hare, trustee |
| Topic 3 | make, sure, statement, progress, difference, northern, ireland, towards, representations, responsibilities | statement, make, sure, progress, ireland, representations, difference, northern, milton, departmental |
| Topic 4 | debt, water, credit, charges, pay, loan, loans, people, financial, cost | payday, loan, lenders, debts, loans, debt, charges, water, high-cost, creditors |
| Topic 5 | house, committee, parliament, leader, select, motion, parliamentary, debate, scrutiny, business | select, leader, house, motion, committee, backbench, scrutiny, committees, benchers, parliamentary |
| Topic 6 | new, development, work, need, investment, strategy, must, programme, working, also | development, strategy, develop, project, regional, projects, partnership, together, developed, build |
| Topic 7 | road, petition, residents, car, vehicles, petitioners, dogs, roads, site, house | petitioners, lap-dancing, petition, dogs, dog, pedestrians, cycling, declares, drivers, accidents |
| Topic 8 | important, agree, welcome, country, making, particularly, thank, part, makes, good | agree, welcome, important, absolutely, makes, making, friend’s, thank, particularly, giving |
| Topic 9 | companies, market, company, competition, energy, consumers, prices, price, consumer, customers | competition, companies, market, wholesale, suppliers, company, regulator, ofgem, supplier, consumers |
| Topic 10 | women, men, equality, women’s, discrimination, rights, gender, equal, woman, marriage | gender, bishops, transgender, women’s, women, abortion, same-sex, marriage, equality, gay |
| Topic 11 | energy, climate, fuel, change, green, carbon, emissions, gas, environmental, industry | renewables, solar, insulation, feed-in, biofuels, greenhouse, dioxide, kyoto, carbon, climate |
| Topic 12 | office, post, offices, royal, service, closure, mail, services, network, christmas | offices, mail, sub-post, post, sub-postmasters, closures, consignia, swindon, closure, office |
| Topic 13 | mr, north, south, east, west, spoke, friends, birmingham, talked, central | ealing, spoke, dorset, lothian, ayrshire, glasgow, chris, southwark, pontefract, birmingham |
| Topic 14 | pension, scheme, benefit, pensions, benefits, pensioners, system, credit, allowance, income | pension, esa, pensions, claimants, retirement, pip, pensioners, incapacity, dwp, means-testing |
| Topic 15 | economy, jobs, economic, growth, unemployment, country, investment, chancellor, budget, crisis | unemployment, recession, growth, economy, obr, deficit, inflation, economic, forecast, borrowing |
| Topic 16 | schools, school, education, children, teachers, parents, pupils, educational, special, primary | academies, pupil, grammar, schools, pupils, teachers, ofsted, school, teacher, sen |
| Topic 17 | want, say, one, think, know, need, us, get, go, see | think, say, things, want, something, saying, going, lot, really, go |
| Topic 18 | review, report, commission, independent, process, recommendations, inquiry, also, system, standards | recommendations, inquiry, panel, audit, independent, recommendation, reviews, fsa, complaints, review |
| Topic 19 | business, businesses, small, financial, bank, banks, insurance, rates, industry, enterprise | smes, medium-sized, businesses, bank, enterprises, enterprise, banking, rbs, business, rock |
| Topic 20 | wales, industry, welsh, north-east, england, assembly, constituency, jobs, manufacturing, uk | welsh, wales, steel, cardiff, north-east, assembly, visteon, newcastle, manufacturing, tyneside |
| Topic 21 | care, services, social, mental, need, health, home, provision, service, older | mental, care, social, elderly, older, advocacy, services, residential, palliative, discharges |
| Topic 22 | pay, work, workers, employment, working, wage, minimum, employers, paid, national | wage, workers, zero-hours, employees, paternity, employer, minimum, employers, employment, workplace |
| Topic 23 | amendment, clause, amendments, new, 1, lords, section, 2, act, clauses | amendment, nos, insert, subsection, clause, amendments, clauses, section, lords, schedule |
| Topic 24 | report, last, since, said, received, published, year, following, official, end | march, vol, official, january, july, november, published, december, june, october |
| Topic 25 | made, clear, impact, decision, changes, recent, assessment, decisions, effect, proposed | made, decision, assessment, clear, decisions, impact, implications, recent, changes, effect |
| Topic 26 | funding, cuts, fund, cut, budget, grant, spending, bbc, review, flood | flood, funding, bbc, formula, grant, flooding, floods, cumbria, lottery, grants |
| Topic 27 | money, spent, extra, spend, liberal, cost, spending, value, opposition, tory | money, spent, liberal, spend, democrats, tories, tory, lib, democrat, conservatives |
| Topic 28 | constituency, great, community, proud, many, sport, one, also, world, new | maiden, arts, football, museum, museums, sport, olympic, games, sports, heritage |
| Topic 29 | families, child, poverty, children, parents, work, credit, working, family, living | lone, poverty, childcare, families, low-income, child, nursery, four-year-olds, nurseries, joseph |
| Topic 30 | party, conservative, vote, parliament, political, election, labour, parties, scottish, elected | party, vote, voting, conservative, party’s, voters, election, voted, votes, politics |
| Topic 31 | point, can, may, issue, take, however, whether, matter, understand, consider | matter, point, understand, consider, certainly, accept, possible, issue, course, happy |
| Topic 32 | member, said, lady, mentioned, raised, comments, speech, referred, points, remarks | member, lady, comments, remarks, bromley, interesting, chislehurst, pointed, front-bench, mentioned |
| Topic 33 | european, uk, eu, countries, united, union, europe, states, british, trade | accession, enlargement, wto, lisbon, treaty, eu, doha, european, negotiations, brexit |
| Topic 34 | education, skills, young, training, students, university, college, higher, science, apprenticeships | ema, fe, students, apprenticeship, universities, qualifications, apprenticeships, graduates, vocational, courses |
| Topic 35 | local, authorities, authority, planning, community, communities, councils, area, guidance, system | authorities, local, authority, planning, councils, councillors, locally, guidance, localism, communities |
| Topic 36 | disabled, carers, disability, support, disabilities, needs, caring, autism, learning, can | carers, autism, autistic, disabled, disabilities, disability, dementia, carer, caring, deaf |
| Topic 37 | environment, marine, fishing, sea, industry, natural, fish, countryside, rural, fisheries | fishermen, cod, forestry, biodiversity, habitats, mmo, fishing, fish, cfp, fisheries |
| Topic 38 | justice, court, violence, victims, cases, criminal, domestic, courts, prison, offence | attorney-general, defendants, defendant, prison, prosecutors, solicitor-general, stalking, prosecutor, prisons, prosecution |
| Topic 39 | international, foreign, rights, human, peace, un, conflict, world, aid, war | israel, palestinian, israeli, gaza, sri, zimbabwe, iran, yemen, hamas, palestinians |
| Topic 40 | day, family, never, told, families, life, happened, constituent, man, went | man, died, son, story, stories, hillsborough, tragedy, daughter, husband, angry |
| Topic 41 | proposals, future, forward, consultation, plans, meet, paper, current, discuss, bring | proposals, consultation, paper, plans, forward, discuss, white, proposal, meet, implement |
| Topic 42 | behaviour, crime, antisocial, alcohol, young, drugs, drug, problem, use, tackle | antisocial, asbos, alcohol, alcohol-related, binge, psychoactive, drinking, fireworks, behaviour, graffiti |
| Topic 43 | housing, homes, social, affordable, private, home, accommodation, rent, need, properties | housing, tenants, rented, tenancies, homelessness, leasehold, landlords, rents, properties, leaseholders |
| Topic 44 | question, order, mr, put, asked, answer, questions, ask, speaker, time | question, answer, questions, speaker, asked, deputy, answers, order, apologise, read |
| Topic 45 | research, cancer, treatment, medical, condition, screening, disease, can, patients, use | embryos, prostate, cervical, hepatitis, cloning, transplant, embryo, fertilisation, embryonic, endometriosis |
| Topic 46 | online, internet, farmers, animals, digital, animal, broadband, sites, tickets, technology | cull, badgers, badger, fur, bovine, mink, culling, circuses, touts, snares |
| Topic 47 | defence, forces, armed, plymouth, personnel, service, military, army, nuclear, royal | mod, naval, hms, submarines, dockyard, veterans, armed, plymouth, covenant, personnel |
| Topic 48 | information, home, security, data, immigration, control, orders, system, terrorism, appeal | extradition, tpims, sia, warrant, detention, checks, tpim, terrorism, intercept, identity |
| Topic 49 | police, officers, crime, policing, home, force, service, forces, officer, chief | constable, constables, officers, policing, police, soca, ipcc, constabulary, pcsos, hmic |
| Topic 50 | nhs, hospital, patients, health, services, hospitals, care, service, trust, trusts | dentists, dentistry, pharmacies, pct, nhs, hospitals, hospital, dental, trusts, patients |
| Topic 51 | tax, budget, cut, chancellor, cuts, rate, income, vat, benefit, hit | 50p, vat, millionaires, hit, tax, allowances, credits, richest, chancellor, ifs |
| Topic 52 | years, now, two, time, first, three, past, one, months, ago | years, three, months, ago, two, past, weeks, five, four, now |
| Topic 53 | staff, doctors, emergency, medical, service, training, nurses, royal, junior, ambulance | ambulance, junior, staffing, doctors, halifax, posts, nurses, fss, staff, cpr |
| Topic 54 | bill, legislation, act, law, rights, provisions, powers, regulations, place, believe | bill, legislation, bill’s, provisions, passage, regulations, legislative, draft, statute, definition |
| Topic 55 | public, sector, private, organisations, service, voluntary, services, society, community, organisation | public, voluntary, organisations, sector, private, co-operative, volunteering, volunteers, volunteer, co-operatives |
| Topic 56 | health, national, inequalities, programme, suicide, disease, department, prevention, among, risk | flu, hiv, pandemic, inequalities, infections, suicide, mortality, infection, mrsa, vaccine |
| Topic 57 | council, london, areas, city, area, constituency, centre, rural, county, liverpool | county, mayor, borough, cities, liverpool, city, regeneration, council’s, london, towns |
| Topic 58 | advice, legal, cases, civil, hull, aid, case, compensation, claims, service | hull, tribunal, legal, compensation, solicitors, advice, concentrix, servants, lawyers, tribunals |
| Topic 59 | people, work, many, young, get, people’s, can, help, lives, job | people, people’s, get, getting, work, young, jobcentre, lives, youth, find |
| Topic 60 | tax, revenue, relief, duty, uk, avoidance, hmrc, charities, companies, taxation | evasion, hmrc, gaar, avoidance, inland, stamp, revenue, relief, gift, dependencies |
| Topic 61 | government, government’s, policy, labour, previous, scotland, scottish, commitment, policies, coalition | government, previous, policy, government’s, scotland, coalition, scottish, labour, disappointing, administrations |
| Topic 62 | trafficking, home, uk, asylum, refugees, immigration, country, human, migration, britain | trafficking, slavery, trafficked, sierra, leone, slave, dubs, fgm, yarl’s, wilberforce |
| Topic 63 | food, products, industry, smoking, advertising, tobacco, ban, product, standards, shops | gambling, betting, sunbed, tobacco, cocoa, meat, supermarkets, labelling, retailers, packaging |
| Topic 64 | members, debate, many, issues, also, today, heard, opportunity, hope, issue | members, debate, heard, speak, sides, issues, hear, opportunity, listened, pleased |
| Topic 65 | children, child, parents, young, children’s, family, contact, vulnerable, adoption, abuse | csa, adopters, adoption, child’s, cafcass, looked-after, children’s, children, safeguarding, barred |
| Topic 66 | transport, rail, bus, services, line, travel, train, network, passengers, london | rail, passengers, passenger, heathrow, hs2, freight, high-speed, crossrail, airlines, runway |
| Topic 67 | year, million, number, increase, figures, increased, billion, 1, average, cost | million, figures, figure, increased, increase, compared, year, total, fallen, estimates |
| Topic 68 | support, ensure, can, help, aware, taking, take, provide, action, continue | aware, ensure, support, taking, steps, continue, help, action, assure, encourage |
| Topic 69 | deal, recently, new, can, lack, great, concern, done, move, given | deal, recently, lack, elsewhere, concern, great, improved, offered, done, new |
As seen in the word lists above, there is relatively scattershot semantic coherence, although exclusivity is high, when using the 69 topic models suggested by Lee and Mimno’s (2014) algorithm. We therefore re-ran the analysis, using 30 topic models, which has resulted in increased semantic coherence, albeit with slightly lower exclusivity, as illustrated in Figure .
Fruchterman-Reingold plot of K30 Network
Coherence of K30 Topic Models
| Topic Number | AWS Speeches | Percent of AWS Speeches | Non-AWS Speeches | Percent of non-AWS Speeches | Male MP Speeches | Percent of Male MP Speeches |
|---|---|---|---|---|---|---|
| Topic 1 | 1,792 | 3.34% | 1,229 | 4.4% | 8,163 | 4.82% |
| Topic 2 | 2,476 | 4.61% | 2,514 | 9.01% | 11,393 | 6.73% |
| Topic 3 | 1,082 | 2.01% | 632 | 2.27% | 926 | 0.55% |
| Topic 4 | 1,303 | 2.43% | 900 | 3.23% | 3,364 | 1.99% |
| Topic 5 | 1,976 | 3.68% | 1,371 | 4.91% | 9,653 | 5.7% |
| Topic 6 | 1,720 | 3.2% | 623 | 2.23% | 4,562 | 2.69% |
| Topic 7 | 2,721 | 5.07% | 758 | 2.72% | 4,045 | 2.39% |
| Topic 8 | 879 | 1.64% | 381 | 1.37% | 2,193 | 1.29% |
| Topic 9 | 1,008 | 1.88% | 743 | 2.66% | 1,747 | 1.03% |
| Topic 10 | 1,351 | 2.52% | 658 | 2.36% | 6,235 | 3.68% |
| Topic 11 | 2,144 | 3.99% | 1,552 | 5.56% | 4,494 | 2.65% |
| Topic 12 | 2,507 | 4.67% | 883 | 3.16% | 10,394 | 6.14% |
| Topic 13 | 1,231 | 2.29% | 825 | 2.96% | 3,972 | 2.35% |
| Topic 14 | 984 | 1.83% | 646 | 2.32% | 1,570 | 0.93% |
| Topic 15 | 1,180 | 2.2% | 1,410 | 5.05% | 4,935 | 2.91% |
| Topic 16 | 2,175 | 4.05% | 1,302 | 4.67% | 7,547 | 4.46% |
| Topic 17 | 5,309 | 9.89% | 2,357 | 8.45% | 25,255 | 14.91% |
| Topic 18 | 2,362 | 4.4% | 1,003 | 3.59% | 6,230 | 3.68% |
| Topic 19 | 1,183 | 2.2% | 445 | 1.59% | 3,305 | 1.95% |
| Topic 20 | 1,334 | 2.48% | 561 | 2.01% | 2,075 | 1.23% |
| Topic 21 | 4,361 | 8.12% | 1,556 | 5.58% | 11,845 | 6.99% |
| Topic 22 | 977 | 1.82% | 359 | 1.29% | 2,258 | 1.33% |
| Topic 23 | 1,787 | 3.33% | 890 | 3.19% | 6,124 | 3.62% |
| Topic 24 | 813 | 1.51% | 233 | 0.84% | 2,132 | 1.26% |
| Topic 25 | 1,604 | 2.99% | 1,104 | 3.96% | 4,917 | 2.9% |
| Topic 26 | 1,237 | 2.3% | 664 | 2.38% | 1,105 | 0.65% |
| Topic 27 | 668 | 1.24% | 325 | 1.16% | 1,796 | 1.06% |
| Topic 28 | 3,218 | 5.99% | 1,001 | 3.59% | 8,906 | 5.26% |
| Topic 29 | 1,121 | 2.09% | 304 | 1.09% | 4,463 | 2.64% |
| Topic 30 | 1,202 | 2.24% | 673 | 2.41% | 3,746 | 2.21% |
K30 Pyramid Chart
K30 Bar Chart
| Topic Number | Top Ten Words | Top Ten FREX |
|---|---|---|
| Topic 1 | bill, amendment, clause, new, legislation, amendments, act, committee, provisions, 1 | amendment, clause, amendments, clauses, nos, insert, subsection, provisions, bill, tabled |
| Topic 2 | issues, public, information, also, report, review, process, work, need, important | consultation, review, guidance, recommendations, information, considering, decisions, arrangements, framework, detailed |
| Topic 3 | women, men, pay, equality, rights, women’s, discrimination, equal, work, woman | women, equality, gender, equalities, bishops, discrimination, female, women’s, equal, men |
| Topic 4 | police, crime, officers, behaviour, policing, home, antisocial, community, work, force | policing, antisocial, constable, burglary, wardens, crime, constabulary, police, officers, pcsos |
| Topic 5 | european, uk, countries, eu, union, trade, international, united, world, british | treaty, enlargement, wto, lisbon, doha, eu, eu’s, mod, multilateral, accession |
| Topic 6 | transport, london, rail, bus, road, services, line, travel, network, train | rail, bus, passengers, fares, trains, buses, passenger, heathrow, congestion, hs2 |
| Topic 7 | people, work, benefit, pension, benefits, support, disabled, employment, carers, working | disabled, jobcentre, incapacity, carers, pension, claimants, esa, dla, pensions, atos |
| Topic 8 | immigration, safety, uk, asylum, enforcement, home, number, illegal, licensing, animals | dogs, dog, id, visa, fur, mink, hse, sia, seekers, fireworks |
| Topic 9 | health, research, cancer, treatment, medical, disease, can, smoking, patients, people | cancer, diseases, vaccine, flu, embryos, infections, diabetes, palliative, prostate, cervical |
| Topic 10 | government, labour, conservative, party, opposition, policy, government’s, scotland, scottish, members | conservative, liberal, democrats, conservatives, scottish, democrat, scotland, tory, interruption, tories |
| Topic 11 | care, health, nhs, services, service, hospital, patients, staff, trust, social | dentists, ambulance, dentistry, helier, dentist, nurses, hospital, pct, hospitals, dental |
| Topic 12 | member, members, debate, house, mr, committee, said, time, speaker, north | member, speaker, mr, debate, spoke, thoughtful, backbench, debates, madam, select |
| Topic 13 | companies, financial, company, market, scheme, money, debt, consumers, bank, credit | payday, annuity, oft, policyholders, penrose, fca, loan, prepayment, loans, annuities |
| Topic 14 | young, people, health, mental, youth, prison, problems, drugs, alcohol, drug | prisons, probation, cannabis, reoffending, mental, prison, self-harm, youth, alcohol, sentences |
| Topic 15 | cases, court, legal, law, case, justice, evidence, criminal, courts, home | judicial, attorney-general, defendant, extradition, tpims, suspects, court, courts, prosecution, isc |
| Topic 16 | energy, businesses, business, jobs, investment, economy, industry, economic, new, sector | carbon, renewable, renewables, solar, low-carbon, energy, feed-in, manufacturing, steel, businesses |
| Topic 17 | people, want, one, get, know, say, us, many, think, need | things, think, something, get, want, going, really, say, lot, go |
| Topic 18 | education, schools, school, children, training, skills, parents, teachers, students, young | schools, teachers, pupils, curriculum, sen, academies, ofsted, pupil, grammar, attainment |
| Topic 19 | constituency, city, people, many, years, work, centre, one, hull, great | fishermen, cod, hull, plymouth, maiden, fishing, fish, humber, fleetwood, tourism |
| Topic 20 | housing, homes, people, private, london, social, home, affordable, need, accommodation | rent, tenants, landlords, rented, homelessness, homeless, rents, tenancies, housing, tenancy |
| Topic 21 | tax, year, million, government, budget, cuts, cut, poverty, increase, billion | tax, obr, vat, millionaires, 50p, inflation, budget, fiscal, chancellor, cut |
| Topic 22 | food, post, office, rural, petition, offices, farmers, royal, mail, government | petition, farmers, petitioners, meat, cull, labelling, cattle, badger, culling, beef |
| Topic 23 | people, international, human, government, war, rights, country, un, conflict, world | syria, israel, civilians, palestinian, israeli, gaza, sri, holocaust, hatred, sierra |
| Topic 24 | bbc, media, online, internet, sport, access, digital, culture, clubs, football | bbc, games, olympic, gambling, bbc’s, copyright, lap-dancing, broadband, radio, internet |
| Topic 25 | local, authorities, funding, areas, services, council, community, authority, government, communities | local, authorities, funding, councils, grant, authority, formula, deprived, areas, partnership |
| Topic 26 | children, child, families, care, family, parents, violence, support, domestic, victims | trafficked, csa, same-sex, adopters, child, rape, marriages, marriage, sexual, couples |
| Topic 27 | planning, water, development, land, environment, site, sites, flood, environmental, area | forestry, biodiversity, masts, habitats, gypsy, flood, waterways, flooding, marine, mmo |
| Topic 28 | secretary, state, house, last, statement, report, said, now, question, answer | secretary, statement, state, confirm, official, answer, vol, state’s, letter, written |
| Topic 29 | parliament, wales, vote, commission, political, assembly, people, welsh, elected, charities | electoral, polling, gibraltar, voting, assembly, vote, votes, voter, ballot, elections |
| Topic 30 | can, make, ensure, agree, important, take, made, point, sure, welcome | agree, aware, sure, ensure, taking, lady, welcome, steps, point, make |
There do not appear to be substantial or meaningful differences in the speaking styles of female Labour MPs selected through all women short lists when compared to their female colleagues selected through open short lists using LIWC. This is possibly due to the speaking style dominant in British parliamentary debate, which is more formal than the speech used in most day-to-day conversation. LIWC has American developers, and the dictionary may not be able to capture stylistic differences between American and British English, and may not include words commonly used in formal British English speech, limiting its usefulness in a British context.
There is more gender distinction in some selected terms and topics. AWS MPs are far more likely to make reference to their constituency and their constituents. In the debate between whether MPs should be “delegates” or “trustees” – the “mandate-independence controversy” outlined by Pitkin (1967) – the references to their constituents and constituencies suggests AWS MPs shy away from the Burkean concept of trusteeship and see themselves more as strict representatives of their constituents. In Andeweg & Thomassen’s (2005) typology of ex ante/ex post and above/below political representation, AWS MPs lean towards representation “from below”, although their selection process is ex ante/ex post.
AWS MPs refer to their constituents both specifically and in the abstract, particularly when criticising government policy. For example, in debate on 4th March 2015, Gemma Doyle, than the Labour MP for West Dunbartonshire (elected on an AWS in 2010), when asked if she would give way to Conservative MP Stephen Mosley, responded:
No, I will not [give way], because my constituents want me to make these points, not to give more time to Conservative Members.
On 2nd June 2010, during debate on Israel-Palestine, Valerie Vaz, MP for Walsall South:
My constituents want more than pressure. Will the Foreign Secretary come back to the House and report on a timetable for the discussions on a diplomatic solution, just as we did on Ireland?
On 4th April 2001, Betty Williams, member for Conwy from 1997–2010, raised the case of a wilderness guide in her constituency unable to access parts of the countryside due to foot and mouth disease:
Is my right hon. Friend aware that there is continuing concern about the limited access to the countryside and crags of north Wales? May I draw his attention to the circumstances of my constituent, Ric Potter? Like many others, he has had to travel to Scotland, where there is greater access. Will my right hon. Friend help us to enable people such as Ric Potter to find work in outdoor pursuits?
## A topic model with 69 topics, 81607 documents and a 115477 word dictionary.
## Topic 1 Top Words:
## Highest Prob: secretary, state, tell, ministers, given, today, department
## FREX: secretary, state, confirm, tell, ministers, state's, minister's
## Lift: dhar, lowell, qatada's, #nationalistsconfused, 1135, 2.5bn, 36,500
## Score: secretary, state, confirm, state's, tell, ministers, department
## Topic 2 Top Words:
## Highest Prob: safety, register, registration, indicated, registered, electoral, risk
## FREX: registration, indicated, hse, canvass, register, gurkhas, safety
## Lift: aps, hse, 10-litre, 13a, 14,940, 1760, 1867
## Score: safety, registration, register, electoral, indicated, registered, hse
## Topic 3 Top Words:
## Highest Prob: make, sure, statement, progress, difference, northern, ireland
## FREX: statement, make, sure, progress, ireland, representations, difference
## Lift: 101269, 101548, 102938, 103414, 106583, 107305, 109413
## Score: make, statement, progress, sure, ireland, northern, milton
## Topic 4 Top Words:
## Highest Prob: debt, water, credit, charges, pay, loan, loans
## FREX: payday, loan, lenders, debts, loans, debt, charges
## Lift: 1,021, 1,025, 1,106, 1,189, 1,273, 1,385, 1,413
## Score: debt, water, payday, loan, loans, lenders, credit
## Topic 5 Top Words:
## Highest Prob: house, committee, parliament, leader, select, motion, parliamentary
## FREX: select, leader, house, motion, committee, backbench, scrutiny
## Lift: e-petitions, praying-against, sherlock, wednesdays, sittings, thursdays, 10,000-signature
## Score: committee, house, leader, select, scrutiny, parliament, motion
## Topic 6 Top Words:
## Highest Prob: new, development, work, need, investment, strategy, must
## FREX: development, strategy, develop, project, regional, projects, partnership
## Lift: #1,150, 1.245, 1.875, 128406, 131,000, 18-the, 2002-around
## Score: development, regional, investment, strategy, infrastructure, projects, work
## Topic 7 Top Words:
## Highest Prob: road, petition, residents, car, vehicles, petitioners, dogs
## FREX: petitioners, lap-dancing, petition, dogs, dog, pedestrians, cycling
## Lift: 0.037, 0.044, 0fficial, 1,042, 1,072, 1,108, 1,122
## Score: petitioners, petition, dogs, road, residents, dog, declares
## Topic 8 Top Words:
## Highest Prob: important, agree, welcome, country, making, particularly, thank
## FREX: agree, welcome, important, absolutely, makes, making, friend's
## Lift: adequacies, and-importantly-much, ayse, ballantine, bobtail-and, bostrom, bucketfuls
## Score: agree, important, thank, welcome, friend's, absolutely, country
## Topic 9 Top Words:
## Highest Prob: companies, market, company, competition, energy, consumers, prices
## FREX: competition, companies, market, wholesale, suppliers, company, regulator
## Lift: 1,105, 1,345, ashington, boakye, nord, over-charging, price-fixing
## Score: companies, consumers, energy, market, company, prices, competition
## Topic 10 Top Words:
## Highest Prob: women, men, equality, women's, discrimination, rights, gender
## FREX: gender, bishops, transgender, women's, women, abortion, same-sex
## Lift: balmforth, bpas, celebrants, cohabitants, jessy, msafiri, natal
## Score: women, women's, equality, men, gender, discrimination, marriage
## Topic 11 Top Words:
## Highest Prob: energy, climate, fuel, change, green, carbon, emissions
## FREX: renewables, solar, insulation, feed-in, biofuels, greenhouse, dioxide
## Lift: fossil, #2,500, #solar, 1-are, 1,129, 1,214, 1,343
## Score: energy, fuel, carbon, emissions, climate, renewable, renewables
## Topic 12 Top Words:
## Highest Prob: office, post, offices, royal, service, closure, mail
## FREX: offices, mail, sub-post, post, sub-postmasters, closures, consignia
## Lift: #1.8, #210, #450, 1,001, 1,352, 1,639, 1,827
## Score: post, offices, office, mail, closure, postal, sub-post
## Topic 13 Top Words:
## Highest Prob: mr, north, south, east, west, spoke, friends
## FREX: ealing, spoke, dorset, lothian, ayrshire, glasgow, chris
## Lift: argos's, blackford, cairns's, clemency, marxist, no2id, 0.66
## Score: mr, east, north, south, west, spoke, birmingham
## Topic 14 Top Words:
## Highest Prob: pension, scheme, benefit, pensions, benefits, pensioners, system
## FREX: pension, esa, pensions, claimants, retirement, pip, pensioners
## Lift: means-testing, #20,000, #400, 0º, 1,052, 1,366, 1,482
## Score: pension, pensions, pensioners, allowance, scheme, retirement, credit
## Topic 15 Top Words:
## Highest Prob: economy, jobs, economic, growth, unemployment, country, investment
## FREX: unemployment, recession, growth, economy, obr, deficit, inflation
## Lift: 0.76, 0.83, 1,196, 1,319, 1.04, 1.32, 10-about
## Score: economy, jobs, unemployment, growth, economic, recession, chancellor
## Topic 16 Top Words:
## Highest Prob: schools, school, education, children, teachers, parents, pupils
## FREX: academies, pupil, grammar, schools, pupils, teachers, ofsted
## Lift: sen2, 11-plus, leas, pupil, 000-to, 1-regardless, 1,000-pupil
## Score: schools, school, teachers, pupils, children, education, parents
## Topic 17 Top Words:
## Highest Prob: want, say, one, think, know, need, us
## FREX: think, say, things, want, something, saying, going
## Lift: about-part, arguing-i, beneficial-we, career-many, cause-the, clam, compatriot
## Score: think, want, get, say, things, going, us
## Topic 18 Top Words:
## Highest Prob: review, report, commission, independent, process, recommendations, inquiry
## FREX: recommendations, inquiry, panel, audit, independent, recommendation, reviews
## Lift: bourn, cag's, clarke's, clowes, dg, emag, equitable's
## Score: fsa, inquiry, review, commission, recommendations, report, independent
## Topic 19 Top Words:
## Highest Prob: business, businesses, small, financial, bank, banks, insurance
## FREX: smes, medium-sized, businesses, bank, enterprises, enterprise, banking
## Lift: 0.21, 0.84, 1,034, 1,130, 10-fold, 1130, 12.19
## Score: businesses, business, bank, banks, banking, insurance, small
## Topic 20 Top Words:
## Highest Prob: wales, industry, welsh, north-east, england, assembly, constituency
## FREX: welsh, wales, steel, cardiff, north-east, assembly, visteon
## Lift: a55, angharad, bethesda, co-investment, dewhirst, dogger, gorge
## Score: wales, welsh, assembly, manufacturing, steel, north-east, yorkshire
## Topic 21 Top Words:
## Highest Prob: care, services, social, mental, need, health, home
## FREX: mental, care, social, elderly, older, advocacy, services
## Lift: #900,000, 1,051, 1,312, 10,758, 104962, 1089, 13,198
## Score: care, mental, services, social, health, older, homes
## Topic 22 Top Words:
## Highest Prob: pay, work, workers, employment, working, wage, minimum
## FREX: wage, workers, zero-hours, employees, paternity, employer, minimum
## Lift: 6.70, 8.20, 85p, awb, e-balloting, hannett, increments
## Score: wage, workers, employers, employment, pay, employees, minimum
## Topic 23 Top Words:
## Highest Prob: amendment, clause, amendments, new, 1, lords, section
## FREX: amendment, nos, insert, subsection, clause, amendments, clauses
## Lift: 153a, 22a, 287, 50b, 51b, counter-notice, insured's
## Score: clause, amendment, amendments, lords, nos, insert, subsection
## Topic 24 Top Words:
## Highest Prob: report, last, since, said, received, published, year
## FREX: march, vol, official, january, july, november, published
## Lift: 1-2ws, 1,033, 1,099, 1,124,818, 1,337, 1,368,186, 1,595
## Score: report, official, vol, published, march, april, november
## Topic 25 Top Words:
## Highest Prob: made, clear, impact, decision, changes, recent, assessment
## FREX: made, decision, assessment, clear, decisions, impact, implications
## Lift: 104963, 107312, 116,400, 125214, 125828, 125830, 126370
## Score: made, assessment, impact, changes, decision, decisions, clear
## Topic 26 Top Words:
## Highest Prob: funding, cuts, fund, cut, budget, grant, spending
## FREX: flood, funding, bbc, formula, grant, flooding, floods
## Lift: #10.89, #12, #3.3, #bbcdiversity, 1,027, 1,536-will, 1,546
## Score: funding, cuts, flood, bbc, budget, spending, flooding
## Topic 27 Top Words:
## Highest Prob: money, spent, extra, spend, liberal, cost, spending
## FREX: money, spent, liberal, spend, democrats, tories, tory
## Lift: 1-but, 10,309.63, 1228, 158.8, 1763, 18-that, 1979-80
## Score: money, liberal, tory, democrats, conservatives, tories, spending
## Topic 28 Top Words:
## Highest Prob: constituency, great, community, proud, many, sport, one
## FREX: maiden, arts, football, museum, museums, sport, olympic
## Lift: 0.27, 0.51, 1,084, 1,126, 1,468, 1,580-plus, 1,983
## Score: arts, sport, museum, maiden, heritage, football, constituency
## Topic 29 Top Words:
## Highest Prob: families, child, poverty, children, parents, work, credit
## FREX: lone, poverty, childcare, families, low-income, child, nursery
## Lift: 1,000-discriminates, 1,000-not, 1,080, 1,142,600, 1,170, 1,390, 1,664
## Score: poverty, child, families, children, parents, credit, lone
## Topic 30 Top Words:
## Highest Prob: party, conservative, vote, parliament, political, election, labour
## FREX: party, vote, voting, conservative, party's, voters, election
## Lift: alphabetical, gentry, olga, one-party, randomisation, 1,166, 1,294
## Score: party, conservative, vote, scottish, election, elections, political
## Topic 31 Top Words:
## Highest Prob: point, can, may, issue, take, however, whether
## FREX: matter, point, understand, consider, certainly, accept, possible
## Lift: 450,000-in, advised-by, backslid, bill-albeit, bizarre-but, can-enable, cases-roughly
## Score: point, matter, issue, gentleman's, consider, shall, whether
## Topic 32 Top Words:
## Highest Prob: member, said, lady, mentioned, raised, comments, speech
## FREX: member, lady, comments, remarks, bromley, interesting, chislehurst
## Lift: 12.20, 130b, 1991-forcing, 2,784, 54,000-worth, achieved-is, arguments-and
## Score: member, lady, comments, said, speech, raised, points
## Topic 33 Top Words:
## Highest Prob: european, uk, eu, countries, united, union, europe
## FREX: accession, enlargement, wto, lisbon, treaty, eu, doha
## Lift: 13652, 13653, 13654, 1707, balkan, barnier, blackmailing
## Score: eu, european, countries, union, treaty, europe, trade
## Topic 34 Top Words:
## Highest Prob: education, skills, young, training, students, university, college
## FREX: ema, fe, students, apprenticeship, universities, qualifications, apprenticeships
## Lift: ema, #ne, 1,188, 1,308, 1,555-what, 1,740, 1,803
## Score: students, education, young, skills, apprenticeships, training, universities
## Topic 35 Top Words:
## Highest Prob: local, authorities, authority, planning, community, communities, councils
## FREX: authorities, local, authority, planning, councils, councillors, locally
## Lift: achcew, central-local, laa, lsp, maas, observances, place-shaping
## Score: local, authorities, authority, councils, planning, communities, community
## Topic 36 Top Words:
## Highest Prob: disabled, carers, disability, support, disabilities, needs, caring
## FREX: carers, autism, autistic, disabled, disabilities, disability, dementia
## Lift: autism, rnib, #185, #85, #hellomynameis, 1,400-one, 10-person
## Score: carers, disabled, disability, autism, disabilities, caring, dementia
## Topic 37 Top Words:
## Highest Prob: environment, marine, fishing, sea, industry, natural, fish
## FREX: fishermen, cod, forestry, biodiversity, habitats, mmo, fishing
## Lift: aquaculture, arable, bee-friendly, biodiversity, birdlife, bycatch, caterpillar
## Score: marine, fishing, fishermen, fish, fisheries, wildlife, conservation
## Topic 38 Top Words:
## Highest Prob: justice, court, violence, victims, cases, criminal, domestic
## FREX: attorney-general, defendants, defendant, prison, prosecutors, solicitor-general, stalking
## Lift: #9, 0.08, 0.48, 1,046, 1,237, 10,544, 10.15
## Score: violence, prison, court, offence, criminal, rape, victims
## Topic 39 Top Words:
## Highest Prob: international, foreign, rights, human, peace, un, conflict
## FREX: israel, palestinian, israeli, gaza, sri, zimbabwe, iran
## Lift: lankan, saddam, #aleppo, 1,010, 1,476, 1,591, 1224
## Score: un, israel, syria, humanitarian, palestinian, israeli, iraq
## Topic 40 Top Words:
## Highest Prob: day, family, never, told, families, life, happened
## FREX: man, died, son, story, stories, hillsborough, tragedy
## Lift: 10,000-seat, 12-inch, 1234, 1519-20, 1635, 1710, 174,995
## Score: families, holocaust, family, constituent, man, died, mother
## Topic 41 Top Words:
## Highest Prob: proposals, future, forward, consultation, plans, meet, paper
## FREX: proposals, consultation, paper, plans, forward, discuss, white
## Lift: 10.42, 107910, 109648, 114061, 119621, 141605, 141607
## Score: proposals, consultation, plans, future, forward, paper, white
## Topic 42 Top Words:
## Highest Prob: behaviour, crime, antisocial, alcohol, young, drugs, drug
## FREX: antisocial, asbos, alcohol, alcohol-related, binge, psychoactive, drinking
## Lift: acquisitive, addaction, auto, bailes, crawlers, ghb, gilpin
## Score: antisocial, crime, behaviour, alcohol, drug, drugs, cannabis
## Topic 43 Top Words:
## Highest Prob: housing, homes, social, affordable, private, home, accommodation
## FREX: housing, tenants, rented, tenancies, homelessness, leasehold, landlords
## Lift: one-for-one, one-bedroom, rented, right-to-buy, #19, #21.5, #28.5
## Score: housing, homes, tenants, rented, rent, landlords, affordable
## Topic 44 Top Words:
## Highest Prob: question, order, mr, put, asked, answer, questions
## FREX: question, answer, questions, speaker, asked, deputy, answers
## Lift: 11.00, 11.57, 12.26, 1223, 1232, 1412, 1555-56
## Score: question, speaker, mr, answer, deputy, order, questions
## Topic 45 Top Words:
## Highest Prob: research, cancer, treatment, medical, condition, screening, disease
## FREX: embryos, prostate, cervical, hepatitis, cloning, transplant, embryo
## Lift: abnormalities, cystic, embryo, fertilisation, marrow, @cfaware, #500
## Score: cancer, patients, embryos, screening, treatment, tissue, breast
## Topic 46 Top Words:
## Highest Prob: online, internet, farmers, animals, digital, animal, broadband
## FREX: cull, badgers, badger, fur, bovine, mink, culling
## Lift: culling, @daisydumble, @donna_smiley, @jimspin, @leamingtonsbc, @maggieannehayes, @nhconsortium
## Score: farmers, animals, internet, cull, animal, online, badgers
## Topic 47 Top Words:
## Highest Prob: defence, forces, armed, plymouth, personnel, service, military
## FREX: mod, naval, hms, submarines, dockyard, veterans, armed
## Lift: hms, submarine, submarines, 1,000-people, 1,625, 1,705, 10-3
## Score: defence, armed, forces, plymouth, military, personnel, mod
## Topic 48 Top Words:
## Highest Prob: information, home, security, data, immigration, control, orders
## FREX: extradition, tpims, sia, warrant, detention, checks, tpim
## Lift: carlile's, carlile, sia, 10-month, 10,410, 10,500-for, 10.45
## Score: immigration, terrorism, detention, terrorist, tpims, home, security
## Topic 49 Top Words:
## Highest Prob: police, officers, crime, policing, home, force, service
## FREX: constable, constables, officers, policing, police, soca, ipcc
## Lift: 2003-morecambe, 9,650, a19s, ashleys, bigg, bounties, ckp
## Score: police, officers, policing, crime, forces, constable, neighbourhood
## Topic 50 Top Words:
## Highest Prob: nhs, hospital, patients, health, services, hospitals, care
## FREX: dentists, dentistry, pharmacies, pct, nhs, hospitals, hospital
## Lift: acos, bequest, bernstein, bodmin, catto, cayton, chailey
## Score: nhs, patients, hospital, health, patient, hospitals, care
## Topic 51 Top Words:
## Highest Prob: tax, budget, cut, chancellor, cuts, rate, income
## FREX: 50p, vat, millionaires, hit, tax, allowances, credits
## Lift: #840, 0.76p, 1,003, 1,009, 1,226, 1,275, 1,296
## Score: tax, vat, budget, credits, chancellor, cuts, income
## Topic 52 Top Words:
## Highest Prob: years, now, two, time, first, three, past
## FREX: years, three, months, ago, two, past, weeks
## Lift: 10-week-old, 10,616, 11-month, 11-point, 1758, 196b, 63,500
## Score: years, months, two, ago, three, past, weeks
## Topic 53 Top Words:
## Highest Prob: staff, doctors, emergency, medical, service, training, nurses
## FREX: ambulance, junior, staffing, doctors, halifax, posts, nurses
## Lift: #i'm, 03, 1-who, 1,454, 1,631, 10,000-strong, 10,000-with
## Score: staff, doctors, ambulance, nurses, medical, emergency, junior
## Topic 54 Top Words:
## Highest Prob: bill, legislation, act, law, rights, provisions, powers
## FREX: bill, legislation, bill's, provisions, passage, regulations, legislative
## Lift: 1865, 1990s-when, 1998-it, 19may2000, 2003-largely, 2005-have, 29-year
## Score: bill, legislation, provisions, rights, law, powers, regulations
## Topic 55 Top Words:
## Highest Prob: public, sector, private, organisations, service, voluntary, services
## FREX: public, voluntary, organisations, sector, private, co-operative, volunteering
## Lift: 1844, af, carpetbaggers, nebulous, puk, 1075, 170-year
## Score: public, sector, private, voluntary, organisations, service, services
## Topic 56 Top Words:
## Highest Prob: health, national, inequalities, programme, suicide, disease, department
## FREX: flu, hiv, pandemic, inequalities, infections, suicide, mortality
## Lift: kirkley, lowestoft, nihr, acupuncture, influenza, #148, #3.6
## Score: health, vaccine, flu, inequalities, hiv, infection, suicide
## Topic 57 Top Words:
## Highest Prob: council, london, areas, city, area, constituency, centre
## FREX: county, mayor, borough, cities, liverpool, city, regeneration
## Lift: #12,000, #14.4, #356, #38, #5,000, #500,000, #66.6
## Score: london, council, city, regeneration, county, rural, borough
## Topic 58 Top Words:
## Highest Prob: advice, legal, cases, civil, hull, aid, case
## FREX: hull, tribunal, legal, compensation, solicitors, advice, concentrix
## Lift: 0300, 1,997, 1.148, 1.4m, 112.8, 1147, 128,687
## Score: legal, advice, hull, aid, compensation, civil, tribunal
## Topic 59 Top Words:
## Highest Prob: people, work, many, young, get, people's, can
## FREX: people, people's, get, getting, work, young, jobcentre
## Lift: 2,425, 294,488, 3,699, 37,290, 5,320, 50-to-64, 75589
## Score: people, young, work, get, youth, many, people's
## Topic 60 Top Words:
## Highest Prob: tax, revenue, relief, duty, uk, avoidance, hmrc
## FREX: evasion, hmrc, gaar, avoidance, inland, stamp, revenue
## Lift: 1,643, 3.12, 32.2, 44a, 80g, aaronson, aat
## Score: tax, hmrc, avoidance, revenue, relief, evasion, territories
## Topic 61 Top Words:
## Highest Prob: government, government's, policy, labour, previous, scotland, scottish
## FREX: government, previous, policy, government's, scotland, coalition, scottish
## Lift: 2005-perhaps, 80994, actually-that, agency-when, agenda-access, alloway, aware-in
## Score: government, scotland, scottish, labour, policy, government's, previous
## Topic 62 Top Words:
## Highest Prob: trafficking, home, uk, asylum, refugees, immigration, country
## FREX: trafficking, slavery, trafficked, sierra, leone, slave, dubs
## Lift: #7, 0.025, 1-yes, 1,060, 1,483, 1,746, 1.123
## Score: trafficking, refugees, asylum, slavery, trafficked, immigration, sierra
## Topic 63 Top Words:
## Highest Prob: food, products, industry, smoking, advertising, tobacco, ban
## FREX: gambling, betting, sunbed, tobacco, cocoa, meat, supermarkets
## Lift: 0.7p, 00, 0157, 1,000-almost, 1,032, 1,200-i, 1,666
## Score: food, smoking, products, tobacco, advertising, gambling, industry
## Topic 64 Top Words:
## Highest Prob: members, debate, many, issues, also, today, heard
## FREX: members, debate, heard, speak, sides, issues, hear
## Lift: noakes, 6.47pm, accept-or, analysis-but, aspect-listening-is, bunfight, called-making
## Score: members, debate, issues, many, opposition, heard, constituents
## Topic 65 Top Words:
## Highest Prob: children, child, parents, young, children's, family, contact
## FREX: csa, adopters, adoption, child's, cafcass, looked-after, children's
## Lift: csa, @mandatenow, 10-month-old, 10-went, 12j, 150765, 16-only
## Score: children, child, parents, young, children's, adoption, child's
## Topic 66 Top Words:
## Highest Prob: transport, rail, bus, services, line, travel, train
## FREX: rail, passengers, passenger, heathrow, hs2, freight, high-speed
## Lift: 12-car, 15.15, 50.1, adtranz, anti-icing, bahn, chilterns
## Score: rail, transport, bus, passengers, fares, trains, hs2
## Topic 67 Top Words:
## Highest Prob: year, million, number, increase, figures, increased, billion
## FREX: million, figures, figure, increased, increase, compared, year
## Lift: #112, #3,850, #84.3, #87.2, 1,249, 102269, 122.9
## Score: million, year, billion, increase, figures, average, increased
## Topic 68 Top Words:
## Highest Prob: support, ensure, can, help, aware, taking, take
## FREX: aware, ensure, support, taking, steps, continue, help
## Lift: 103684, 103965, 107320, 111532, 112584, 113698, 117890
## Score: support, ensure, steps, aware, help, taking, department
## Topic 69 Top Words:
## Highest Prob: deal, recently, new, can, lack, great, concern
## FREX: deal, recently, lack, elsewhere, concern, great, improved
## Lift: 2004-a, 721,000, added-gva-per, age-of, centres-jacs-which, employment-can, index-the
## Score: deal, recently, new, worktrack, lack, can, great
##
## Call:
## estimateEffect(formula = 1:69 ~ short_list, stmobj = topic_model2,
## metadata = lab_corpus_fem_stm$meta, uncertainty = "Global")
##
##
## Topic 1:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0170490 0.0002872 59.37 <0.0000000000000002 ***
## short_listTRUE 0.0068967 0.0003515 19.62 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 2:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0058952 0.0002397 24.596 <0.0000000000000002 ***
## short_listTRUE 0.0007065 0.0003026 2.335 0.0196 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 3:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0087151 0.0001117 78.033 <0.0000000000000002 ***
## short_listTRUE 0.0002122 0.0001471 1.442 0.149
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 4:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0064766 0.0003078 21.041 <0.0000000000000002 ***
## short_listTRUE 0.0036170 0.0003905 9.262 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 5:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0120578 0.0002548 47.32 <0.0000000000000002 ***
## short_listTRUE 0.0035458 0.0003329 10.65 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 6:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0313826 0.0004269 73.50 <0.0000000000000002 ***
## short_listTRUE -0.0093819 0.0004914 -19.09 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 7:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0082027 0.0003883 21.126 < 0.0000000000000002 ***
## short_listTRUE 0.0024224 0.0004804 5.043 0.00000046 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 8:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0155991 0.0001353 115.293 < 0.0000000000000002 ***
## short_listTRUE -0.0006691 0.0001476 -4.532 0.00000585 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 9:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0106917 0.0002678 39.930 < 0.0000000000000002 ***
## short_listTRUE -0.0025223 0.0003462 -7.285 0.000000000000325 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 10:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0147928 0.0004467 33.12 <0.0000000000000002 ***
## short_listTRUE 0.0002684 0.0005480 0.49 0.624
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 11:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0131074 0.0004309 30.419 <0.0000000000000002 ***
## short_listTRUE -0.0008988 0.0005145 -1.747 0.0806 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 12:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0100377 0.0003281 30.597 < 0.0000000000000002 ***
## short_listTRUE -0.0026488 0.0003772 -7.023 0.00000000000219 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 13:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0084694 0.0002244 37.745 < 0.0000000000000002 ***
## short_listTRUE 0.0015081 0.0002907 5.189 0.000000212 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 14:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0133791 0.0004686 28.55 <0.0000000000000002 ***
## short_listTRUE 0.0060922 0.0005583 10.91 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 15:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0169596 0.0004287 39.556 < 0.0000000000000002 ***
## short_listTRUE 0.0029699 0.0005137 5.782 0.00000000742 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 16:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0135196 0.0005197 26.017 < 0.0000000000000002 ***
## short_listTRUE 0.0032504 0.0006708 4.846 0.00000126 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 17:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0425088 0.0003149 134.990 <0.0000000000000002 ***
## short_listTRUE 0.0008102 0.0003849 2.105 0.0353 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 18:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0268601 0.0004987 53.86 <0.0000000000000002 ***
## short_listTRUE -0.0068300 0.0005996 -11.39 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 19:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0129044 0.0003253 39.673 < 0.0000000000000002 ***
## short_listTRUE -0.0016916 0.0003815 -4.434 0.00000925 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 20:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0086108 0.0003453 24.94 <0.0000000000000002 ***
## short_listTRUE 0.0060656 0.0004412 13.75 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 21:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0124577 0.0003104 40.14 < 0.0000000000000002 ***
## short_listTRUE -0.0011221 0.0003965 -2.83 0.00465 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 22:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0127304 0.0003168 40.18 <0.0000000000000002 ***
## short_listTRUE 0.0008321 0.0004019 2.07 0.0384 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 23:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0185865 0.0005606 33.153 < 0.0000000000000002 ***
## short_listTRUE -0.0028071 0.0006255 -4.488 0.0000072 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 24:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0138634 0.0002294 60.42 < 0.0000000000000002 ***
## short_listTRUE 0.0013380 0.0002841 4.71 0.00000248 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 25:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0111247 0.0001346 82.629 < 0.0000000000000002 ***
## short_listTRUE 0.0007898 0.0001681 4.697 0.00000265 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 26:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0098005 0.0002865 34.206 <0.0000000000000002 ***
## short_listTRUE 0.0037734 0.0003825 9.864 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 27:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0103148 0.0002170 47.54 < 0.0000000000000002 ***
## short_listTRUE 0.0009108 0.0002727 3.34 0.000839 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 28:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0111687 0.0004059 27.517 < 0.0000000000000002 ***
## short_listTRUE 0.0027939 0.0005148 5.428 0.0000000573 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 29:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0105119 0.0003580 29.363 <0.0000000000000002 ***
## short_listTRUE 0.0042201 0.0004476 9.428 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 30:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0169375 0.0004454 38.029 <0.0000000000000002 ***
## short_listTRUE 0.0008002 0.0005608 1.427 0.154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 31:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0369135 0.0002231 165.46 <0.0000000000000002 ***
## short_listTRUE -0.0068067 0.0002839 -23.98 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 32:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0100393 0.0001334 75.244 <0.0000000000000002 ***
## short_listTRUE -0.0002580 0.0001609 -1.603 0.109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 33:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0230342 0.0005126 44.94 <0.0000000000000002 ***
## short_listTRUE -0.0059588 0.0006435 -9.26 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 34:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0136733 0.0004434 30.837 <0.0000000000000002 ***
## short_listTRUE 0.0011096 0.0005518 2.011 0.0444 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 35:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0198252 0.0003413 58.08 <0.0000000000000002 ***
## short_listTRUE -0.0061363 0.0003960 -15.49 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 36:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0061227 0.0003190 19.192 <0.0000000000000002 ***
## short_listTRUE 0.0041027 0.0004135 9.922 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 37:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0069696 0.0003209 21.717 < 0.0000000000000002 ***
## short_listTRUE 0.0014975 0.0004234 3.537 0.000405 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 38:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0226478 0.0005217 43.41 <0.0000000000000002 ***
## short_listTRUE -0.0073624 0.0005950 -12.38 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 39:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0205562 0.0006323 32.510 <0.0000000000000002 ***
## short_listTRUE 0.0011502 0.0008248 1.395 0.163
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 40:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0139283 0.0003594 38.75 <0.0000000000000002 ***
## short_listTRUE 0.0047452 0.0004551 10.43 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 41:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0107945 0.0001176 91.78 <0.0000000000000002 ***
## short_listTRUE -0.0014811 0.0001339 -11.06 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 42:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0150504 0.0004387 34.310 < 0.0000000000000002 ***
## short_listTRUE -0.0033592 0.0005544 -6.059 0.00000000138 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 43:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0140265 0.0004861 28.854 < 0.0000000000000002 ***
## short_listTRUE 0.0021683 0.0006085 3.563 0.000367 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 44:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0147186 0.0002651 55.512 < 0.0000000000000002 ***
## short_listTRUE 0.0016385 0.0003339 4.907 0.000000928 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 45:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0135442 0.0004527 29.917 < 0.0000000000000002 ***
## short_listTRUE -0.0036869 0.0005342 -6.902 0.00000000000517 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 46:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0067855 0.0003691 18.385 <0.0000000000000002 ***
## short_listTRUE 0.0042941 0.0004461 9.625 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 47:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0077717 0.0003512 22.131 < 0.0000000000000002 ***
## short_listTRUE 0.0027458 0.0004467 6.147 0.000000000793 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 48:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0225105 0.0005032 44.74 <0.0000000000000002 ***
## short_listTRUE -0.0077244 0.0005804 -13.31 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 49:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0155733 0.0004778 32.595 < 0.0000000000000002 ***
## short_listTRUE -0.0038787 0.0005826 -6.657 0.000000000028 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 50:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0232451 0.0006510 35.71 <0.0000000000000002 ***
## short_listTRUE -0.0076606 0.0007433 -10.31 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 51:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0105758 0.0003557 29.73 <0.0000000000000002 ***
## short_listTRUE 0.0055875 0.0004505 12.40 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 52:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01749027 0.00018333 95.403 <0.0000000000000002 ***
## short_listTRUE 0.00001783 0.00022567 0.079 0.937
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 53:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0107597 0.0003954 27.214 <0.0000000000000002 ***
## short_listTRUE 0.0003058 0.0004669 0.655 0.513
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 54:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0261021 0.0004258 61.303 <0.0000000000000002 ***
## short_listTRUE -0.0044960 0.0004877 -9.219 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 55:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0101880 0.0001862 54.730 <0.0000000000000002 ***
## short_listTRUE -0.0004807 0.0002428 -1.979 0.0478 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 56:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0114028 0.0003551 32.109 < 0.0000000000000002 ***
## short_listTRUE -0.0015309 0.0004383 -3.493 0.000479 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 57:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0129042 0.0003259 39.591 <0.0000000000000002 ***
## short_listTRUE 0.0012948 0.0004148 3.121 0.0018 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 58:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0090822 0.0002441 37.205 < 0.0000000000000002 ***
## short_listTRUE 0.0015927 0.0003317 4.801 0.00000158 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 59:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0166452 0.0001943 85.65 < 0.0000000000000002 ***
## short_listTRUE 0.0007286 0.0002343 3.11 0.00187 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 60:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0100794 0.0003770 26.735 <0.0000000000000002 ***
## short_listTRUE -0.0002398 0.0004647 -0.516 0.606
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 61:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0112640 0.0001145 98.35 <0.0000000000000002 ***
## short_listTRUE 0.0019036 0.0001402 13.58 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 62:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0104597 0.0003952 26.468 <0.0000000000000002 ***
## short_listTRUE -0.0010403 0.0004701 -2.213 0.0269 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 63:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0088016 0.0003625 24.283 < 0.0000000000000002 ***
## short_listTRUE 0.0020139 0.0004635 4.345 0.000014 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 64:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0207819 0.0002268 91.627 < 0.0000000000000002 ***
## short_listTRUE 0.0017483 0.0002992 5.844 0.00000000513 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 65:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0136872 0.0003768 36.325 < 0.0000000000000002 ***
## short_listTRUE -0.0017374 0.0004535 -3.831 0.000128 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 66:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0132110 0.0004840 27.295 < 0.0000000000000002 ***
## short_listTRUE 0.0037754 0.0005862 6.441 0.00000000012 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 67:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0219834 0.0003086 71.241 < 0.0000000000000002 ***
## short_listTRUE -0.0011517 0.0003885 -2.965 0.00303 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 68:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0193971 0.0001786 108.61 <0.0000000000000002 ***
## short_listTRUE -0.0026874 0.0002288 -11.75 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 69:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00275112 0.00002015 136.555 <0.0000000000000002 ***
## short_listTRUE -0.00002558 0.00002598 -0.985 0.325
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## A topic model with 30 topics, 81607 documents and a 115477 word dictionary.
## Topic 1 Top Words:
## Highest Prob: bill, amendment, clause, new, legislation, amendments, act
## FREX: amendment, clause, amendments, clauses, nos, insert, subsection
## Lift: #185, #85, 0.003, 05, 1-competences, 1-impact, 1,924
## Score: clause, amendment, amendments, bill, provisions, lords, nos
## Topic 2 Top Words:
## Highest Prob: issues, public, information, also, report, review, process
## FREX: consultation, review, guidance, recommendations, information, considering, decisions
## Lift: 1-who, 1,842, 109648, 1402, 151387, 1981-was, 1a-has
## Score: consultation, guidance, information, review, committee, issues, process
## Topic 3 Top Words:
## Highest Prob: women, men, pay, equality, rights, women's, discrimination
## FREX: women, equality, gender, equalities, bishops, discrimination, female
## Lift: gender, #112, #neverthelesshepersisted, 1-breast-feed, 1,087, 1,574, 1.57
## Score: women, women's, equality, men, gender, discrimination, girls
## Topic 4 Top Words:
## Highest Prob: police, crime, officers, behaviour, policing, home, antisocial
## FREX: policing, antisocial, constable, burglary, wardens, crime, constabulary
## Lift: 1,113, 1.24, 17,614, acpo's, adz, alcohol-free, alleygator
## Score: police, crime, officers, policing, antisocial, behaviour, constable
## Topic 5 Top Words:
## Highest Prob: european, uk, countries, eu, union, trade, international
## FREX: treaty, enlargement, wto, lisbon, doha, eu, eu's
## Lift: #420, 0.26, 0.56, 07, 09, 1-2, 1-of
## Score: eu, european, countries, treaty, armed, defence, forces
## Topic 6 Top Words:
## Highest Prob: transport, london, rail, bus, road, services, line
## FREX: rail, bus, passengers, fares, trains, buses, passenger
## Lift: #145, 0.1p, 0.45, 0.86, 1-very, 1,122, 1,658
## Score: rail, transport, bus, passengers, fares, trains, congestion
## Topic 7 Top Words:
## Highest Prob: people, work, benefit, pension, benefits, support, disabled
## FREX: disabled, jobcentre, incapacity, carers, pension, claimants, esa
## Lift: dla, #400, 0300, 1-to-1, 1,030, 1,052, 1,366
## Score: pension, carers, disabled, pensions, allowance, disability, credit
## Topic 8 Top Words:
## Highest Prob: immigration, safety, uk, asylum, enforcement, home, number
## FREX: dogs, dog, id, visa, fur, mink, hse
## Lift: 44a, a8, acoba, arcs, attachment-free, bareboat, bonfires
## Score: immigration, asylum, animals, dogs, fireworks, dog, animal
## Topic 9 Top Words:
## Highest Prob: health, research, cancer, treatment, medical, disease, can
## FREX: cancer, diseases, vaccine, flu, embryos, infections, diabetes
## Lift: 1169, 20-fold, ablation, abnormalities, adpkd, aed, anaesthesia
## Score: cancer, patients, disease, smoking, health, diagnosis, screening
## Topic 10 Top Words:
## Highest Prob: government, labour, conservative, party, opposition, policy, government's
## FREX: conservative, liberal, democrats, conservatives, scottish, democrat, scotland
## Lift: #nationalistsconfused, 1-but, 1.135, 10,182, 10.91, 1125, 116385
## Score: conservative, scottish, party, labour, government, scotland, liberal
## Topic 11 Top Words:
## Highest Prob: care, health, nhs, services, service, hospital, patients
## FREX: dentists, ambulance, dentistry, helier, dentist, nurses, hospital
## Lift: 2.24, 2005-6, 22,600, 422, 5.45pm, 8.03, 8.41
## Score: nhs, patients, care, hospital, health, patient, hospitals
## Topic 12 Top Words:
## Highest Prob: member, members, debate, house, mr, committee, said
## FREX: member, speaker, mr, debate, spoke, thoughtful, backbench
## Lift: e-petitions, @daisydumble, @percyblakeney63, 10,000-signature, 1028, 1080, 11.00
## Score: member, mr, committee, members, speaker, debate, house
## Topic 13 Top Words:
## Highest Prob: companies, financial, company, market, scheme, money, debt
## FREX: payday, annuity, oft, policyholders, penrose, fca, loan
## Lift: fca, oft, prepayment, #1.8, #20,000, 0.21, 0.84
## Score: companies, consumers, fsa, banks, company, customers, consumer
## Topic 14 Top Words:
## Highest Prob: young, people, health, mental, youth, prison, problems
## FREX: prisons, probation, cannabis, reoffending, mental, prison, self-harm
## Lift: cannabis, hawton, poppers, camhs, inmates, reoffending, #230
## Score: young, mental, prison, drugs, alcohol, youth, drug
## Topic 15 Top Words:
## Highest Prob: cases, court, legal, law, case, justice, evidence
## FREX: judicial, attorney-general, defendant, extradition, tpims, suspects, court
## Lift: 110-day, abscond, absconded, acquittals, adduce, anti-viral, babar
## Score: court, offence, courts, criminal, justice, prosecution, offences
## Topic 16 Top Words:
## Highest Prob: energy, businesses, business, jobs, investment, economy, industry
## FREX: carbon, renewable, renewables, solar, low-carbon, energy, feed-in
## Lift: fossil, sellafield, viyella, energy-intensive, low-carbon, #12.5, #140,000
## Score: energy, businesses, jobs, economy, manufacturing, industry, investment
## Topic 17 Top Words:
## Highest Prob: people, want, one, get, know, say, us
## FREX: things, think, something, get, want, going, really
## Lift: 1,027, 2.85, 30s-will, 6.37, 778, about-part, accept-there
## Score: people, get, think, things, going, want, say
## Topic 18 Top Words:
## Highest Prob: education, schools, school, children, training, skills, parents
## FREX: schools, teachers, pupils, curriculum, sen, academies, ofsted
## Lift: ema, #8,000, 1,000-pupil, 1,051, 1,100-i, 1,170, 1,204
## Score: schools, school, education, children, teachers, pupils, students
## Topic 19 Top Words:
## Highest Prob: constituency, city, people, many, years, work, centre
## FREX: fishermen, cod, hull, plymouth, maiden, fishing, fish
## Lift: #14.4, #66.6, 0.27, 0.51, 1,084, 1,126, 1.41
## Score: plymouth, constituency, hull, city, fishing, fish, arts
## Topic 20 Top Words:
## Highest Prob: housing, homes, people, private, london, social, home
## FREX: rent, tenants, landlords, rented, homelessness, homeless, rents
## Lift: right-to-buy, #19, #21.5, #28.5, 1,000-odd, 1,026, 1,083
## Score: housing, homes, rented, rent, tenants, landlords, affordable
## Topic 21 Top Words:
## Highest Prob: tax, year, million, government, budget, cuts, cut
## FREX: tax, obr, vat, millionaires, 50p, inflation, budget
## Lift: 0.38, 1,869, 107,500, 11.2, 13,600, 2,073, 2.33
## Score: tax, cuts, budget, poverty, chancellor, unemployment, billion
## Topic 22 Top Words:
## Highest Prob: food, post, office, rural, petition, offices, farmers
## FREX: petition, farmers, petitioners, meat, cull, labelling, cattle
## Lift: #450, 1072, 11,900, 12-point, 934, a690, ablewell
## Score: food, farmers, petitioners, petition, post, rural, offices
## Topic 23 Top Words:
## Highest Prob: people, international, human, government, war, rights, country
## FREX: syria, israel, civilians, palestinian, israeli, gaza, sri
## Lift: muslims, #aleppo, #no2lgbthate, 0.002, 1,000-almost, 1,010, 1,019
## Score: syria, un, israel, humanitarian, iraq, palestinian, israeli
## Topic 24 Top Words:
## Highest Prob: bbc, media, online, internet, sport, access, digital
## FREX: bbc, games, olympic, gambling, bbc's, copyright, lap-dancing
## Lift: age-restricted, age-verification, aquatics, bacta, bandwidth, bbfc, bduk
## Score: bbc, sport, tickets, internet, digital, online, football
## Topic 25 Top Words:
## Highest Prob: local, authorities, funding, areas, services, council, community
## FREX: local, authorities, funding, councils, grant, authority, formula
## Lift: 416,000, 596,000, 82-3, 885, allison's, baccy, bellwin
## Score: local, authorities, funding, councils, authority, council, services
## Topic 26 Top Words:
## Highest Prob: children, child, families, care, family, parents, violence
## FREX: trafficked, csa, same-sex, adopters, child, rape, marriages
## Lift: @mandatenow, 1-regardless, 1,000-discriminates, 1,142,600, 1,483, 1,746, 10-month-old
## Score: child, children, parents, violence, care, sexual, rape
## Topic 27 Top Words:
## Highest Prob: planning, water, development, land, environment, site, sites
## FREX: forestry, biodiversity, masts, habitats, gypsy, flood, waterways
## Lift: biodiversity, encampments, masts, #tartantories, 0fficial, 1,000-year-old, 1,251
## Score: planning, land, flood, marine, sites, water, site
## Topic 28 Top Words:
## Highest Prob: secretary, state, house, last, statement, report, said
## FREX: secretary, statement, state, confirm, official, answer, vol
## Lift: 12.40, ashleys, ayia, burne, cabinet's, cairns's, clutha
## Score: secretary, state, statement, answer, confirm, inquiry, leader
## Topic 29 Top Words:
## Highest Prob: parliament, wales, vote, commission, political, assembly, people
## FREX: electoral, polling, gibraltar, voting, assembly, vote, votes
## Lift: @leamingtonsbc, @maggieannehayes, @nhconsortium, #keeptweeting, 1-46, 1-would, 1,294
## Score: electoral, vote, elections, wales, assembly, referendum, welsh
## Topic 30 Top Words:
## Highest Prob: can, make, ensure, agree, important, take, made
## FREX: agree, aware, sure, ensure, taking, lady, welcome
## Lift: 1565, 19602, 2,095, 42931, 94254, agencies-an, anguish-filled
## Score: agree, aware, thank, ensure, point, lady, can
##
## Call:
## estimateEffect(formula = 1:30 ~ short_list, stmobj = topic_model_k30,
## metadata = lab_corpus_fem_stm$meta, uncertainty = "Global")
##
##
## Topic 1:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0449702 0.0006448 69.739 <0.0000000000000002 ***
## short_listTRUE -0.0068689 0.0008156 -8.422 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 2:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0757500 0.0006134 123.48 <0.0000000000000002 ***
## short_listTRUE -0.0215054 0.0007219 -29.79 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 3:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0230229 0.0006035 38.149 <0.0000000000000002 ***
## short_listTRUE -0.0007421 0.0006919 -1.073 0.283
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 4:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0289385 0.0006492 44.578 <0.0000000000000002 ***
## short_listTRUE -0.0074447 0.0007523 -9.896 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 5:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0368452 0.0006689 55.085 < 0.0000000000000002 ***
## short_listTRUE -0.0051017 0.0007838 -6.509 0.0000000000759 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 6:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0217916 0.0005761 37.829 < 0.0000000000000002 ***
## short_listTRUE 0.0054441 0.0007613 7.151 0.000000000000868 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 7:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0298344 0.0006489 45.97 <0.0000000000000002 ***
## short_listTRUE 0.0131773 0.0007897 16.69 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 8:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0186028 0.0004845 38.398 <0.0000000000000002 ***
## short_listTRUE 0.0004560 0.0006261 0.728 0.466
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 9:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0249526 0.0005936 42.033 < 0.0000000000000002 ***
## short_listTRUE -0.0051042 0.0007216 -7.073 0.00000000000153 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 10:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0378866 0.0004700 80.618 < 0.0000000000000002 ***
## short_listTRUE 0.0019999 0.0005925 3.375 0.000738 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 11:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0408025 0.0006947 58.736 <0.0000000000000002 ***
## short_listTRUE -0.0083331 0.0008785 -9.486 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 12:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0390032 0.0006049 64.47 <0.0000000000000002 ***
## short_listTRUE 0.0082105 0.0007367 11.14 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 13:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0302322 0.0006073 49.78 < 0.0000000000000002 ***
## short_listTRUE -0.0028580 0.0007663 -3.73 0.000192 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 14:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0239348 0.0005284 45.296 < 0.0000000000000002 ***
## short_listTRUE -0.0032166 0.0006376 -5.045 0.000000456 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 15:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0409453 0.0006981 58.66 <0.0000000000000002 ***
## short_listTRUE -0.0167479 0.0008565 -19.55 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 16:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0384177 0.0006692 57.406 < 0.0000000000000002 ***
## short_listTRUE -0.0046120 0.0008693 -5.305 0.000000113 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 17:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0788485 0.0005733 137.533 < 0.0000000000000002 ***
## short_listTRUE 0.0019968 0.0007196 2.775 0.00552 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 18:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0298568 0.0006882 43.381 < 0.0000000000000002 ***
## short_listTRUE 0.0037965 0.0008478 4.478 0.00000755 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 19:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0196088 0.0005073 38.65 <0.0000000000000002 ***
## short_listTRUE 0.0092533 0.0006687 13.84 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 20:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0204491 0.0005531 36.970 < 0.0000000000000002 ***
## short_listTRUE 0.0038428 0.0007524 5.107 0.000000328 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 21:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0460876 0.0008011 57.53 <0.0000000000000002 ***
## short_listTRUE 0.0142956 0.0010103 14.15 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 22:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0139586 0.0004706 29.661 <0.0000000000000002 ***
## short_listTRUE 0.0055231 0.0005725 9.647 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 23:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0261614 0.0006770 38.645 <0.0000000000000002 ***
## short_listTRUE 0.0007679 0.0008461 0.908 0.364
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 24:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0124067 0.0003947 31.436 <0.0000000000000002 ***
## short_listTRUE 0.0049008 0.0004951 9.899 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 25:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0426172 0.0006188 68.88 <0.0000000000000002 ***
## short_listTRUE -0.0085936 0.0007492 -11.47 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 26:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.02501676 0.00056573 44.220 <0.0000000000000002 ***
## short_listTRUE -0.00003169 0.00073090 -0.043 0.965
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 27:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0149391 0.0004739 31.523 <0.0000000000000002 ***
## short_listTRUE 0.0003263 0.0005921 0.551 0.582
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 28:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0427024 0.0005845 73.06 <0.0000000000000002 ***
## short_listTRUE 0.0145518 0.0007268 20.02 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 29:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0188018 0.0005255 35.777 <0.0000000000000002 ***
## short_listTRUE 0.0062939 0.0006902 9.118 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Topic 30:
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0526689 0.0003202 164.510 <0.0000000000000002 ***
## short_listTRUE -0.0037508 0.0003916 -9.577 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
A random selection of 2% of all references to “my constituency”, “my constituent” and “my constituents”, by AWS MPs, in context.
| Pre | Keyword | Post |
|---|---|---|
| Liverpool and many other places ? In one instance in | my constituency | , a rent officer accompanying a landlady , who was |
| not reduced . When I speak with young people in | my constituency | , I am often struck by the seriousness of the |
| ? It rings very hollowly in Newcastle where many of | my constituents | cannot even plan for the next week , because they |
| pharmaceutical industry is a major contributor to our economy . | My constituency | is also home to the university of Hertfordshire , a |
| run its course . People running domestic violence refuges in | my constituency | are desperately worried that those refuges will not be there |
| my personal views and political instincts , and conflicted by | my constituency’s | large vote to remain and my country’s narrow vote to |
| a more local job . The Wigan area , which | my constituency | covers in part , is the part of the north-west |
| Deputy Speaker . Baroness Jay visited a local nursery in | my constituency | . We saw-it was fantastic-an independent nursery sharing premises with |
| other mothers and who are a particularly vulnerable group in | my constituency | ? |
| greater community participation " to mean that people in | my constituency | will have a fairer say in developments and that they |
| n " , " Airedale general hospital is also in | my constituency | . It is the biggest employer in my constituency , |
| Are the Government aware that many young Asian ladies in | my constituency | would like a change in immigration regulations to prevent those |
| that raising aspiration is crucial . It is crucial in | my constituency | , which suffered 18 years of under-investment under a Conservative |
| all of equal status . I cherish the diversity in | my constituency | and throughout the country . I am yet more determined |
| this year of 74 per cent . Another school in | my constituency | , however , has been in special measures , and |
| If it were to be used to help people in | my constituency | who are struggling with increased care costs and who are |
| " However , today I want to talk about how | my constituency | can once again make Britain great and how it can |
| one has had a particularly terrible impact on one of | my constituents | , Doris Armstrong , who has successfully seen him off |
| and SNP Members . I am talking about jobs in | my constituency | and not about independence , which is why I am |
| life chances agenda . I recently visited a school in | my constituency | , a primary school in Moss Side , that had |
| . I want to refer to the Pont valley in | my constituency | by way of illustration . It is a beautiful valley |
| was not included , and it is little comfort to | my constituents | around the River Cocker and in Winmarleigh and Thurnham , |
| have greater-than-average difficulties with basic skills . A playwright from | my constituency | , Sue Ton , has brought her award-winning play " |
| made on planning last week . Can he explain why | my constituents | , who are now going to have to live 150 |
| . What would my right hon . Friend say to | my constituents | about their fears ? |
| that pay a fair day’s wage . Weekly pay in | my constituency | is significantly below the national average , and the consequences |
| majority of the substantial and very diverse Muslim communities in | my constituency | are united in abhorring terrorism . Indeed , many members |
| the 1-2-3 formation . They were the best innovation in | my constituency | in the past 20 years . They tackle not only |
| form by far the largest part of my correspondence with | my constituents | . I have called a number of public meetings since |
| anywhere . n " , " The diversity of | my constituency | is one of the reasons why it is the best |
| I hope that a strong message will go out to | my constituents | and other policyholders across the country of the deep seriousness |
| two valleys has attracted the television and film industry to | my constituency | . I am pleased about that because the television and |
| to your indulgence , and that of the House and | my constituents | , I was able to recover and return these past |
| My constituent | who turned 60 this year has not received any information | |
| One of | my constituents | who needs IVF treatment is in the unfortunate position of |
| Earlier in business questions , I raised the case of | my constituents | , Mike and Tina Trowhill . I had raised the |
| , including magnificent Ilkley , and its moor , in | my constituency | . The air ambulance helicopter is crucial for carrying patients |
| Royal Navy surface fleet . That has caused concern in | my constituency-Portsmouth | is the historic home of the Royal Navy-coming as it |
| them sleepless nights . Can my hon . Friend offer | my constituents | assurances that the Licensing Act will also address those problems |
| just winding up now . n " , " | My constituents | are being asked to make sacrifices that they can ill |
| choose one’s lawyer has also been mentioned . One of | my constituents | , who is a solicitor , told me of a |
| is a particular concern . The town of Eastwood in | my constituency | faces the loss of its ambulance station . Does my |
| alternative arrangements . What assurances can the Prime Minister give | my constituents | that cuts to higher education will not affect students ? |
| there , and both the schools I attended are in | my constituency | . I am now a mum , bringing up my |
| Care Cymru , which is a local charity based in | my constituency | and which funds specialist cancer nurses to work closely with |
| on the closure of special schools , those children in | my constituency | would be denied their brand-new school and all the opportunities |
| British citizens of Kashmiri extraction have made their home in | my constituency | , and I take an interest on their behalf , |
| in the future , but more than 4,000 pensioners in | my constituency | have benefited from the pension credit , with an average |
| . I had an email this morning from one of | my constituents | saying that her husband had taken his life on Friday |
| Three sub-post offices are earmarked for closure in | my constituency | , and one of them serves the most disadvantaged community |
| It seems , however , that at the very least | my constituent | has been given some very shoddy treatment , and it |
| is a design and creation company on Silicon roundabout in | my constituency | . It employs 65 people and recently produced a film |
| ) : this is a pernicious and divisive measure . | My constituents | are saying to me , " Why am I being |
| enough pressure on young people trying to find work in | my constituency | . Figures last month showed an increase of 467 % |
| " Up to 1,000 households in | my constituency | face having their tax credits withdrawn this year , and |
| to open yet another children’s centre in the network in | my constituency | . They are doing extraordinarily important outreach work , particularly |
| thank the Secretary of State for his reply . In | my constituency | , 6,563 over 75-year-olds would benefit by £ 200 a |
| . For example , one of the major employers in | my constituency-if | not the single largest direct employer in our county-is a |
| violence against women . It concerns me that women in | my constituency | who have been victims of rape cannot gain access to |
| It is perhaps a reflection on | my constituency | that I have many constituents who have been caught up |
| Friend for her reply . A lot of people in | my constituency | still enjoy using cheques , which is a good thing |
| alarmed . I was clear that I did not want | my constituency | to be asset-stripped . Equally , I do not hold |
| forward some of my concerns about striking a balance . | My constituents | and , I imagine , quite a number of others |
| removed from schools in the Bradford district , which includes | my constituency | . |
| our prisons , nothing that happened in Winson Green in | my constituency | on Friday came as a shock . The independent monitoring |
| want to illustrate the other things that the plant in | my constituency | is doing . We have just secured £ 50 million |
| Friend the Member for Dudley , South . Many of | my constituents | from Brierley Hill , Amblecote and Quarry Bank learned to |
| The vicar at St Lawrence church in Darlaston in | my constituency | has to cover All Saints in Darlaston and All Saints |
| dissatisfaction . I also thank a number of groups in | my constituency-Enfield | Disability Action , Enfield transport users group and the Enfield |
| recognition , and I pay tribute to those people in | my constituency | and the officers on the council who have worked closely |
| takes less than half an hour for 83 % of | my constituents | to get to court . n " ) |
| on them already . It does not seem fair that | my constituents | should bear that burden . For that reason , I |
| will be reduced . Haulier companies such as Owens in | my constituency | , which employs 500 people , pay a massive amount |
| have a duty to legislate . I feel strongly about | my constituents | being conned-there is no other word for it . I |
| in Northampton need help with debt problems . Many of | my constituents | have no savings , but are sold more credit than |
| . It is privatisation that Londoners do not want . | My constituents | tell me that they want London Underground to stay in |
| there will be no social care in whole villages in | my constituency | . We are told that the Chancellor is minded to |
| will continue to push , because from the perspective of | my constituency | , where we have extraordinarily high house prices , if |
| did the same at Alma primary school-both of them in | my constituency | . I participated in a literacy hour lesson in both |
| appropriate to speak during the industry and environment debate since | my constituency | of Burnley is famed for its industrial legacy and its |
| . I know , from speaking to disability organisations in | my constituency | in Milton Keynes , that they very much appreciate the |
| local constituency MP . I was visited in Liverpool by | my constituents | Pat and Terry Hughes , who told me their story |
| since the very sad death of Jade Lomas Anderson in | my constituency | I have done a great deal of work on this |
| and organised crime , which are blighting many areas in | my constituency | and need to be tackled urgently . I also welcome |
| along with other measures , has cut significantly unemployment in | my constituency | . It has also enabled us to spend less on |
| sparsely populated areas are left out . In short , | my constituents | have been ill served by deregulation . For Sheffield , |
| position . I have delivered petitions to this House from | my constituents | because bus services have been savagely cut in my constituency |
| credit unions will be supported and allowed to expand . | My constituency | has two excellent and progressive credit unions , Unify and |
| to consider how the regulator is responding to requests from | my constituents | , who have to wait until the gas supplier receives |
| such as bonuses . However , I have defended to | my constituents | the fact that if the Government had not stepped in |
| hon . Friend comment on the situation of one of | my constituents | , who murdered a man ? The murderer , my |
| under threat at present and the incomes of some of | my constituents | who are not involved in agriculture have already been reduced |
| ? I attended last week with a bereaved family from | my constituency | , who were very impressed . Does my right hon |
| how difficult this is for real people . I asked | my constituents | how they are managing their energy costs , which we |
| On Friday I attended the opening of a micro-business in | my constituency | , Little Rembrandt’s Café in Ferryhill . That business opened |
| poor state of bus services in Tyne and Wear and | my constituency | . The Transport Act 1985 did not deliver on its |
| hon . Friend throw any light on that problem for | my constituents | ? |
| , there has been an overall fall in unemployment in | my constituency | of 37.7 per cent . If we had not lost |
| State’s comments in as far as they go . In | my constituency | , there is undoubtedly huge concern about the misuse of |
| , as well as the maximum number of jobs in | my constituency | ? Can he also tell the House what further he |
| the problems experienced with digital television reception in parts of | my constituency | . A constituent of mine , Mr . John Preece |
| changes are having in my constituency right now , and | my constituents | know what impact they are having . Action to tackle |
| and challenging it . I have taken the views of | my constituents | very seriously , but next week I intend to vote |
| some form of foundation course in the UK . In | my constituency | , Twin Training International Ltd provides such courses , along |
| In | my constituency | , 31 % of the children-more than 6,000 children - |
| ensure that the country is kept safe and secure . | My constituents | none the less have concerns , which I should like |
| the A and E department at Trafford General hospital in | my constituency | . n " , " Over the years , |
| and not to rely on discretionary payments , so that | my constituents | and others across the country can have a home that |
| leisure facility in one of the most beautiful parts of | my constituency | , even though no one wanted it or could afford |
| south Wales , with the losses in Llanwern , in | my constituency | , very much wrapped up in the announcement of the |
| improve the lives of a substantial number of people in | my constituency | . I also thought that it might illuminate and teach |
| but it is not the reality for too many of | my constituents | . We have seen a massive increase in precarious employment |
| county ? Ilkley is in a very beautiful part of | my constituency | , but the economic boom in Leeds and the excellent |
| Is my right hon . Friend aware that , in | my constituency | of Luton , South , the incidence of tuberculosis is |
| c ( " | My constituent | who has been campaigning on portability of care packages outwith |
| lot of work with a carers ’ support group in | my constituency | , Durham and Chester-le-Street Carers Support . Does my hon |
| are a matter of life and death for many of | my constituents | . I am therefore not surprised that more than 32,000 |
| some issues , not least for Capel Manor college in | my constituency | , which is the only specialist horticultural college in London |
| our young people ? " A secondary school in | my constituency | named after Admiral Lord Nelson has as its motto , |
| The future of manufacturing industry in | my constituency | is in the high-tech , high-skilled industries . Would my |
| " , " I want to talk about tourism in | my constituency | . Tourism was mentioned earlier ; we know that VAT |
| A number of | my constituents | who have been given leave to remain in this country |
| , and also on access to jobs for many of | my constituents | , because when people factor in having to pay the |
| , " Where are the private sector jobs ? In | my constituency | and that of my hon . Friend the Member for |
| ones that surround it . I thought it important that | my constituents | ’ views about how their money is spent on their |
| in 1956 , and I know that manufacturing employers in | my constituency | are equally outraged by the Secretary of State’s proposals . |
| an excellent measure , and I know that many of | my constituents | will be pleased to see it come into force . |
| science and technology in this country . This is what | my constituents | are telling me . The hon . Gentleman can choose |
| Disorder Act 1998 . Some projects have been funded in | my constituency | by sources such as the neighbourhood renewal fund , and |
| c ( " Many of | my constituents | value highly the local radio station , BBC Radio Humberside-no |
| very aware that both Faye Turney and Arthur Batchelor are | my constituents | , so I was in close contact with the media |
| much power just in transmission . As a gentleman in | my constituency | says , it is always either windy , sunny or |
| not , among other things , ambassadors for Parliament ? | My constituents | cannot understand why we cannot introduce changes to use our |
| the businesses that create those jobs . Although many of | my constituents | are poor , they have no poverty of ambition , |
| have experienced with planning applications for new housing developments in | my constituency | , which all too often are poorly designed , lack |
| " I welcome this opportunity to raise the concerns of | my constituents | who have difficulty in finding an NHS dentist . This |
| the decision . That will not meet the needs of | my constituents | , either , and I cannot support the police authority |
| the issues of dog welfare and dog control that put | my constituents | at risk ; and , worst of all , it |
| August 2006 , Luke Molnar , the 17-year-old son of | my constituents | Gill and Steve Molnar , died on the island of |
| have so many challenges across the country , including in | my constituency | ? |
| Act 2016 will do huge damage to my city and | my constituency | . It pulls the rug out from under Londoners on |
| . Friend will be aware that the two boroughs in | my constituency | , Westminster and Kensington and Chelsea , both suffered as |
| Let me tell the House about one hard-working family in | my constituency | that this Government are squeezing . Mark Thomas works for |
| social issues , which is important for the families in | my constituency | . I especially welcome the children’s tax credit , which |
| backgrounds , including poor backgrounds , and is representative of | my constituency | . That is the sort of school that Labour Members |
| . I recently saw that very clearly in Chirk in | my constituency | at what we will always think of as Cadbury’s , |
| the London borough of Merton , half of which covers | my constituency | , dealing with incidents of graffiti has become increasingly challenging |
| Minister may not know or understand is that many of | my constituents | do not have cars or the money to take several |
| that subject certainly came up in the rural parts of | my constituency | . I have spoken in this place before about Broadband |
| I have described , and frustration on the part of | my constituent | , we received the letter dated 18 September that I |
| going to cope in the coming weeks and months . | My constituency | now has a food bank-the first ever in modern Oldham-and |
| c ( " No subject impacts more on | my constituents | than the cost of living . Wages are dropping-in the |
| we were in an interview recently . John Ruane from | my constituency | has a brain tumour , which means that he has |
| of Scotland in Partington-one of the most deprived areas of | my constituency-has | just announced closure plans . Will he ensure that there |
| has also contributed to the campaign . In Scotland , | my constituent | Rose Stallard has been campaigning on the matter . Recently |
| Carillion Energy , which is headquartered in | my constituency | , is undoubtedly keen to benefit from the Minister’s TLC |
| the acute hospitals to improve the hearing aid services for | my constituents | . My constituency is in an old industrial area where |
| and has continued to fail people in the area such | my constituent | , Lisa Welsh , and her neighbours , who have |
| HMRC office , with the loss of 60 staff . | My constituent | Sahin Kathawala has said that she may not even qualify |
| and take-up work , which has been well received in | my constituency | ? Will my hon . Friend confirm that this tax |
| My constituent | Richard Freeman has not seen his son in six years | |
| This morning , a constituent contacted | my constituency | office threatening to commit suicide because they were so depressed |
| who are working hard to improve the local communities in | my constituency | and the lives of local residents and I am proud |
| Part of | my constituency | had a bad experience with the Big Lottery Fund , |
| , " I talked to Lordswood school for girls in | my constituency | , which still enters every pupil for at least one |
| Childminders in | my constituency | are very pro what they do and also pro the |
| of the poverty trap . n " , " | My constituents | are virtually imprisoned by the excessively high rents charged for |
| to rectify the mistake . Without the food banks in | my constituency | , run by St George’s Crypt , St Bartholomew’s church |
| that I want to describe some of the problems that | my constituents | face . At present , unemployment stands at 10 per |
| more universal . I have had discussions with pensioners in | my constituency | , and a couple of other points arise out of |
| people who live in Slough . More than half of | my constituents | , if they marry someone from overseas , will be |
| was convicted in court in Llandudno last week of assaulting | my constituent | , Mr . Mark Aelwyn Roberts , causing him actual |
| There are very few local sources of employment , so | my constituents | from Little Hulton have to travel to get to work |
| , especially over the last 10 years and unemployment in | my constituency | is now at its lowest level for 10 years . |
| constituency . No job losses have occurred at Dewhirst in | my constituency | . Hopefully , with the help of the Assembly and |
| of individuals who have fallen foul of Concentrix . In | my constituent’s | case , her tax credits were cancelled while she was |
| that for myself when I am out and about in | my constituency | , and I am sure that the same applies to |
| school , where one of the teachers is another of | my constituents-Mrs | . Eileen Hosie . I have visited the school , |
| of the country , but my understanding from schools in | my constituency | is that they are gaining no net benefit from the |
| with young children in pushchairs . Indeed , many of | my constituents | still hold on to the romantic notion of a return |
| fault of her own . n " , " | My constituent | was of course very concerned , anxious and depressed about |
| creating the right environment to encourage investment . Corus in | my constituency | has up-to-date equipment and , at the home of the |
| just what a fantastic contribution he made to ensuring that | my constituents | knew that I was fighting on their behalf to keep |
| the naval base review , which has great significance for | my constituency | . Last year , Labour’s defence budget reached £ 30 |
| bills , and high inflation more generally , many of | my constituents | are having to make painful savings in their household budgets |
| to stress from the outset that I do not believe | my constituent’s | experience is an isolated one , and wish to take |
| My constituents | will be very disappointed that electrification will be starting in | |
| to welcome the Minister with responsibility for rural communities to | my constituency | in the recess for a rural conference . I welcome |
| Last summer | my constituent | , Mr Kenneth Bailey , suffered a major stroke while |
| want to thank my hon . Friend for coming to | my constituency | recently to give a talk on the implications of independence |
| c ( " Absolutely . | My constituency | happens to have that tricky combination : short , steep |
| powers for PCSOs , who do an excellent job in | my constituency | and deserve the tools they need to do the job |
| proposals to change the sentencing guidelines . I know that | my constituents | whose three-year-old daughter was assaulted by Craig Sweeney will particularly |
| through her course , as are many young people in | my constituency | . I tell Ministers that the issue is pressing and |
| book . " n " , " In | my constituency | , the conspirators at Denby Poultry Products made hundreds of |
| issues that related to young people from my experience in | my constituency | . I visited a school and read out a poem |
| from the schools councils of all the secondary schools in | my constituency | . When I met them two weeks ago I expected |
| and say that in the small city of Dunblane in | my constituency | . |
| be aware that I have been campaigning for justice for | my constituent | , Michael Shields , who has now been transferred from |
| third sector IT training company for young people based in | my constituency | , told me of a young man who comes from |
| this approach , and I am sure that companies in | my constituency | will also do so , especially those operating in ship |
| a number of Remploy factories , including the one in | my constituency | . The local trade union rep from GMB has not |
| to rent a modest three-bedroom property in Finsbury Park in | my constituency | . This is unaffordable and represents a failure of the |
| intended to be up and running early in April ? | My constituents | who suffer from this disease want to know how to |
| Over the weekend , a number of | my constituents | have contacted me with concerns about the adequacy of security |
| it is perfectly possible that one of the wards in | my constituency | , East Ecclesfield , could end up being split into |
| proposals to close City pool by 2016 , and in | my constituency | to reduce funding for Newburn leisure centre while seeking alternative |
| £ 72,000 plus accommodation costs will not help many of | my constituents | , particularly those on lower or middle incomes . |
| ( John Robertson ) for the free ATM machines throughout | my constituency | , and I hope that there will soon be more |
| n " , " In her letter to me , | my constituent | said : n " , " " We |
| , there was the impact of women working . In | my constituency | , many women work , many in part-time jobs , |
| credit . These are all incredibly valuable measures , and | my constituents | are talking about them and have high expectations from them |
| hon . Friend for that reply . For years , | my constituents | have been saying to me that they have saved for |
| a week for a GP appointment , and some in | my constituency | are waiting two weeks . A third cannot even get |
| Friend describes is also being experienced by GP practices in | my constituency | . GP practice managers have told me that the system |
| I welcome the Minister’s apology today , but how can | my constituents | and my police and crime commissioner and South Wales police |
| and Pensions about what it will do to ensure that | my constituents | and people everywhere get the support that they desperately need |
| of any borough in London , and the academies in | my constituency | are struggling . They have not delivered the soaring GCSE |
| make a great deal of difference to nurses working in | my constituency | . Does the Chancellor agree that it is in the |
| to the industry . The effect could be dramatic in | my constituency | . I only hope that the Chancellor has thought the |
| . The Park Wall North surface mine near Crook in | my constituency | is available for sale as part of a package consisting |
| , which is putting up our sugar prices and putting | my constituents | out of work . Can the Secretary of State assure |
| support for a substantial local application . My community and | my constituents | are thoroughly sick of the lack of support at national |
| difficult to fill vacancies . Nevertheless , many employers in | my constituency | have the mindset that older workers should not be considered |
| is in a Liberal Democrat constituency-but it serves half of | my constituents | . The health establishment has for many years scorned Mitcham |
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